# -*- 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 logging import json # type: ignore from google.auth.transport.requests import AuthorizedSession # type: ignore from google.auth import credentials as ga_credentials # type: ignore from google.api_core import exceptions as core_exceptions from google.api_core import retry as retries from google.api_core import rest_helpers from google.api_core import rest_streaming from google.api_core import gapic_v1 import cloudsdk.google.protobuf from cloudsdk.google.protobuf import json_format from requests import __version__ as requests_version import dataclasses from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union import warnings from google.api import httpbody_pb2 # type: ignore from googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types import prediction_service from google.longrunning import operations_pb2 # type: ignore from .rest_base import _BasePredictionServiceRestTransport from .base import DEFAULT_CLIENT_INFO as BASE_DEFAULT_CLIENT_INFO 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 = logging.getLogger(__name__) DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo( gapic_version=BASE_DEFAULT_CLIENT_INFO.gapic_version, grpc_version=None, rest_version=f"requests@{requests_version}", ) if hasattr(DEFAULT_CLIENT_INFO, "protobuf_runtime_version"): # pragma: NO COVER DEFAULT_CLIENT_INFO.protobuf_runtime_version = cloudsdk.google.protobuf.__version__ class PredictionServiceRestInterceptor: """Interceptor for PredictionService. Interceptors are used to manipulate requests, request metadata, and responses in arbitrary ways. Example use cases include: * Logging * Verifying requests according to service or custom semantics * Stripping extraneous information from responses These use cases and more can be enabled by injecting an instance of a custom subclass when constructing the PredictionServiceRestTransport. .. code-block:: python class MyCustomPredictionServiceInterceptor(PredictionServiceRestInterceptor): def pre_chat_completions(self, request, metadata): logging.log(f"Received request: {request}") return request, metadata def post_chat_completions(self, response): logging.log(f"Received response: {response}") return response def pre_count_tokens(self, request, metadata): logging.log(f"Received request: {request}") return request, metadata def post_count_tokens(self, response): logging.log(f"Received response: {response}") return response def pre_direct_predict(self, request, metadata): logging.log(f"Received request: {request}") return request, metadata def post_direct_predict(self, response): logging.log(f"Received response: {response}") return response def pre_direct_raw_predict(self, request, metadata): logging.log(f"Received request: {request}") return request, metadata def post_direct_raw_predict(self, response): logging.log(f"Received response: {response}") return response def pre_embed_content(self, request, metadata): logging.log(f"Received request: {request}") return request, metadata def post_embed_content(self, response): logging.log(f"Received response: {response}") return response def pre_explain(self, request, metadata): logging.log(f"Received request: {request}") return request, metadata def post_explain(self, response): logging.log(f"Received response: {response}") return response def pre_fetch_predict_operation(self, request, metadata): logging.log(f"Received request: {request}") return request, metadata def post_fetch_predict_operation(self, response): logging.log(f"Received response: {response}") return response def pre_generate_content(self, request, metadata): logging.log(f"Received request: {request}") return request, metadata def post_generate_content(self, response): logging.log(f"Received response: {response}") return response def pre_invoke(self, request, metadata): logging.log(f"Received request: {request}") return request, metadata def post_invoke(self, response): logging.log(f"Received response: {response}") return response def pre_predict(self, request, metadata): logging.log(f"Received request: {request}") return request, metadata def post_predict(self, response): logging.log(f"Received response: {response}") return response def pre_predict_long_running(self, request, metadata): logging.log(f"Received request: {request}") return request, metadata def post_predict_long_running(self, response): logging.log(f"Received response: {response}") return response def pre_raw_predict(self, request, metadata): logging.log(f"Received request: {request}") return request, metadata def post_raw_predict(self, response): logging.log(f"Received response: {response}") return response def pre_server_streaming_predict(self, request, metadata): logging.log(f"Received request: {request}") return request, metadata def post_server_streaming_predict(self, response): logging.log(f"Received response: {response}") return response def pre_stream_generate_content(self, request, metadata): logging.log(f"Received request: {request}") return request, metadata def post_stream_generate_content(self, response): logging.log(f"Received response: {response}") return response def pre_stream_raw_predict(self, request, metadata): logging.log(f"Received request: {request}") return request, metadata def post_stream_raw_predict(self, response): logging.log(f"Received response: {response}") return response transport = PredictionServiceRestTransport(interceptor=MyCustomPredictionServiceInterceptor()) client = PredictionServiceClient(transport=transport) """ def pre_chat_completions(self, request: prediction_service.ChatCompletionsRequest, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[prediction_service.ChatCompletionsRequest, Sequence[Tuple[str, Union[str, bytes]]]]: """Pre-rpc interceptor for chat_completions Override in a subclass to manipulate the request or metadata before they are sent to the PredictionService server. """ return request, metadata def post_chat_completions(self, response: rest_streaming.ResponseIterator) -> rest_streaming.ResponseIterator: """Post-rpc interceptor for chat_completions DEPRECATED. Please use the `post_chat_completions_with_metadata` interceptor instead. Override in a subclass to read or manipulate the response after it is returned by the PredictionService server but before it is returned to user code. This `post_chat_completions` interceptor runs before the `post_chat_completions_with_metadata` interceptor. """ return response def post_chat_completions_with_metadata(self, response: rest_streaming.ResponseIterator, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[rest_streaming.ResponseIterator, Sequence[Tuple[str, Union[str, bytes]]]]: """Post-rpc interceptor for chat_completions Override in a subclass to read or manipulate the response or metadata after it is returned by the PredictionService server but before it is returned to user code. We recommend only using this `post_chat_completions_with_metadata` interceptor in new development instead of the `post_chat_completions` interceptor. When both interceptors are used, this `post_chat_completions_with_metadata` interceptor runs after the `post_chat_completions` interceptor. The (possibly modified) response returned by `post_chat_completions` will be passed to `post_chat_completions_with_metadata`. """ return response, metadata def pre_count_tokens(self, request: prediction_service.CountTokensRequest, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[prediction_service.CountTokensRequest, Sequence[Tuple[str, Union[str, bytes]]]]: """Pre-rpc interceptor for count_tokens Override in a subclass to manipulate the request or metadata before they are sent to the PredictionService server. """ return request, metadata def post_count_tokens(self, response: prediction_service.CountTokensResponse) -> prediction_service.CountTokensResponse: """Post-rpc interceptor for count_tokens DEPRECATED. Please use the `post_count_tokens_with_metadata` interceptor instead. Override in a subclass to read or manipulate the response after it is returned by the PredictionService server but before it is returned to user code. This `post_count_tokens` interceptor runs before the `post_count_tokens_with_metadata` interceptor. """ return response def post_count_tokens_with_metadata(self, response: prediction_service.CountTokensResponse, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[prediction_service.CountTokensResponse, Sequence[Tuple[str, Union[str, bytes]]]]: """Post-rpc interceptor for count_tokens Override in a subclass to read or manipulate the response or metadata after it is returned by the PredictionService server but before it is returned to user code. We recommend only using this `post_count_tokens_with_metadata` interceptor in new development instead of the `post_count_tokens` interceptor. When both interceptors are used, this `post_count_tokens_with_metadata` interceptor runs after the `post_count_tokens` interceptor. The (possibly modified) response returned by `post_count_tokens` will be passed to `post_count_tokens_with_metadata`. """ return response, metadata def pre_direct_predict(self, request: prediction_service.DirectPredictRequest, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[prediction_service.DirectPredictRequest, Sequence[Tuple[str, Union[str, bytes]]]]: """Pre-rpc interceptor for direct_predict Override in a subclass to manipulate the request or metadata before they are sent to the PredictionService server. """ return request, metadata def post_direct_predict(self, response: prediction_service.DirectPredictResponse) -> prediction_service.DirectPredictResponse: """Post-rpc interceptor for direct_predict DEPRECATED. Please use the `post_direct_predict_with_metadata` interceptor instead. Override in a subclass to read or manipulate the response after it is returned by the PredictionService server but before it is returned to user code. This `post_direct_predict` interceptor runs before the `post_direct_predict_with_metadata` interceptor. """ return response def post_direct_predict_with_metadata(self, response: prediction_service.DirectPredictResponse, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[prediction_service.DirectPredictResponse, Sequence[Tuple[str, Union[str, bytes]]]]: """Post-rpc interceptor for direct_predict Override in a subclass to read or manipulate the response or metadata after it is returned by the PredictionService server but before it is returned to user code. We recommend only using this `post_direct_predict_with_metadata` interceptor in new development instead of the `post_direct_predict` interceptor. When both interceptors are used, this `post_direct_predict_with_metadata` interceptor runs after the `post_direct_predict` interceptor. The (possibly modified) response returned by `post_direct_predict` will be passed to `post_direct_predict_with_metadata`. """ return response, metadata def pre_direct_raw_predict(self, request: prediction_service.DirectRawPredictRequest, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[prediction_service.DirectRawPredictRequest, Sequence[Tuple[str, Union[str, bytes]]]]: """Pre-rpc interceptor for direct_raw_predict Override in a subclass to manipulate the request or metadata before they are sent to the PredictionService server. """ return request, metadata def post_direct_raw_predict(self, response: prediction_service.DirectRawPredictResponse) -> prediction_service.DirectRawPredictResponse: """Post-rpc interceptor for direct_raw_predict DEPRECATED. Please use the `post_direct_raw_predict_with_metadata` interceptor instead. Override in a subclass to read or manipulate the response after it is returned by the PredictionService server but before it is returned to user code. This `post_direct_raw_predict` interceptor runs before the `post_direct_raw_predict_with_metadata` interceptor. """ return response def post_direct_raw_predict_with_metadata(self, response: prediction_service.DirectRawPredictResponse, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[prediction_service.DirectRawPredictResponse, Sequence[Tuple[str, Union[str, bytes]]]]: """Post-rpc interceptor for direct_raw_predict Override in a subclass to read or manipulate the response or metadata after it is returned by the PredictionService server but before it is returned to user code. We recommend only using this `post_direct_raw_predict_with_metadata` interceptor in new development instead of the `post_direct_raw_predict` interceptor. When both interceptors are used, this `post_direct_raw_predict_with_metadata` interceptor runs after the `post_direct_raw_predict` interceptor. The (possibly modified) response returned by `post_direct_raw_predict` will be passed to `post_direct_raw_predict_with_metadata`. """ return response, metadata def pre_embed_content(self, request: prediction_service.EmbedContentRequest, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[prediction_service.EmbedContentRequest, Sequence[Tuple[str, Union[str, bytes]]]]: """Pre-rpc interceptor for embed_content Override in a subclass to manipulate the request or metadata before they are sent to the PredictionService server. """ return request, metadata def post_embed_content(self, response: prediction_service.EmbedContentResponse) -> prediction_service.EmbedContentResponse: """Post-rpc interceptor for embed_content DEPRECATED. Please use the `post_embed_content_with_metadata` interceptor instead. Override in a subclass to read or manipulate the response after it is returned by the PredictionService server but before it is returned to user code. This `post_embed_content` interceptor runs before the `post_embed_content_with_metadata` interceptor. """ return response def post_embed_content_with_metadata(self, response: prediction_service.EmbedContentResponse, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[prediction_service.EmbedContentResponse, Sequence[Tuple[str, Union[str, bytes]]]]: """Post-rpc interceptor for embed_content Override in a subclass to read or manipulate the response or metadata after it is returned by the PredictionService server but before it is returned to user code. We recommend only using this `post_embed_content_with_metadata` interceptor in new development instead of the `post_embed_content` interceptor. When both interceptors are used, this `post_embed_content_with_metadata` interceptor runs after the `post_embed_content` interceptor. The (possibly modified) response returned by `post_embed_content` will be passed to `post_embed_content_with_metadata`. """ return response, metadata def pre_explain(self, request: prediction_service.ExplainRequest, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[prediction_service.ExplainRequest, Sequence[Tuple[str, Union[str, bytes]]]]: """Pre-rpc interceptor for explain Override in a subclass to manipulate the request or metadata before they are sent to the PredictionService server. """ return request, metadata def post_explain(self, response: prediction_service.ExplainResponse) -> prediction_service.ExplainResponse: """Post-rpc interceptor for explain DEPRECATED. Please use the `post_explain_with_metadata` interceptor instead. Override in a subclass to read or manipulate the response after it is returned by the PredictionService server but before it is returned to user code. This `post_explain` interceptor runs before the `post_explain_with_metadata` interceptor. """ return response def post_explain_with_metadata(self, response: prediction_service.ExplainResponse, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[prediction_service.ExplainResponse, Sequence[Tuple[str, Union[str, bytes]]]]: """Post-rpc interceptor for explain Override in a subclass to read or manipulate the response or metadata after it is returned by the PredictionService server but before it is returned to user code. We recommend only using this `post_explain_with_metadata` interceptor in new development instead of the `post_explain` interceptor. When both interceptors are used, this `post_explain_with_metadata` interceptor runs after the `post_explain` interceptor. The (possibly modified) response returned by `post_explain` will be passed to `post_explain_with_metadata`. """ return response, metadata def pre_fetch_predict_operation(self, request: prediction_service.FetchPredictOperationRequest, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[prediction_service.FetchPredictOperationRequest, Sequence[Tuple[str, Union[str, bytes]]]]: """Pre-rpc interceptor for fetch_predict_operation Override in a subclass to manipulate the request or metadata before they are sent to the PredictionService server. """ return request, metadata def post_fetch_predict_operation(self, response: operations_pb2.Operation) -> operations_pb2.Operation: """Post-rpc interceptor for fetch_predict_operation DEPRECATED. Please use the `post_fetch_predict_operation_with_metadata` interceptor instead. Override in a subclass to read or manipulate the response after it is returned by the PredictionService server but before it is returned to user code. This `post_fetch_predict_operation` interceptor runs before the `post_fetch_predict_operation_with_metadata` interceptor. """ return response def post_fetch_predict_operation_with_metadata(self, response: operations_pb2.Operation, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[operations_pb2.Operation, Sequence[Tuple[str, Union[str, bytes]]]]: """Post-rpc interceptor for fetch_predict_operation Override in a subclass to read or manipulate the response or metadata after it is returned by the PredictionService server but before it is returned to user code. We recommend only using this `post_fetch_predict_operation_with_metadata` interceptor in new development instead of the `post_fetch_predict_operation` interceptor. When both interceptors are used, this `post_fetch_predict_operation_with_metadata` interceptor runs after the `post_fetch_predict_operation` interceptor. The (possibly modified) response returned by `post_fetch_predict_operation` will be passed to `post_fetch_predict_operation_with_metadata`. """ return response, metadata def pre_generate_content(self, request: prediction_service.GenerateContentRequest, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[prediction_service.GenerateContentRequest, Sequence[Tuple[str, Union[str, bytes]]]]: """Pre-rpc interceptor for generate_content Override in a subclass to manipulate the request or metadata before they are sent to the PredictionService server. """ return request, metadata def post_generate_content(self, response: prediction_service.GenerateContentResponse) -> prediction_service.GenerateContentResponse: """Post-rpc interceptor for generate_content DEPRECATED. Please use the `post_generate_content_with_metadata` interceptor instead. Override in a subclass to read or manipulate the response after it is returned by the PredictionService server but before it is returned to user code. This `post_generate_content` interceptor runs before the `post_generate_content_with_metadata` interceptor. """ return response def post_generate_content_with_metadata(self, response: prediction_service.GenerateContentResponse, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[prediction_service.GenerateContentResponse, Sequence[Tuple[str, Union[str, bytes]]]]: """Post-rpc interceptor for generate_content Override in a subclass to read or manipulate the response or metadata after it is returned by the PredictionService server but before it is returned to user code. We recommend only using this `post_generate_content_with_metadata` interceptor in new development instead of the `post_generate_content` interceptor. When both interceptors are used, this `post_generate_content_with_metadata` interceptor runs after the `post_generate_content` interceptor. The (possibly modified) response returned by `post_generate_content` will be passed to `post_generate_content_with_metadata`. """ return response, metadata def pre_invoke(self, request: prediction_service.InvokeRequest, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[prediction_service.InvokeRequest, Sequence[Tuple[str, Union[str, bytes]]]]: """Pre-rpc interceptor for invoke Override in a subclass to manipulate the request or metadata before they are sent to the PredictionService server. """ return request, metadata def post_invoke(self, response: rest_streaming.ResponseIterator) -> rest_streaming.ResponseIterator: """Post-rpc interceptor for invoke DEPRECATED. Please use the `post_invoke_with_metadata` interceptor instead. Override in a subclass to read or manipulate the response after it is returned by the PredictionService server but before it is returned to user code. This `post_invoke` interceptor runs before the `post_invoke_with_metadata` interceptor. """ return response def post_invoke_with_metadata(self, response: rest_streaming.ResponseIterator, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[rest_streaming.ResponseIterator, Sequence[Tuple[str, Union[str, bytes]]]]: """Post-rpc interceptor for invoke Override in a subclass to read or manipulate the response or metadata after it is returned by the PredictionService server but before it is returned to user code. We recommend only using this `post_invoke_with_metadata` interceptor in new development instead of the `post_invoke` interceptor. When both interceptors are used, this `post_invoke_with_metadata` interceptor runs after the `post_invoke` interceptor. The (possibly modified) response returned by `post_invoke` will be passed to `post_invoke_with_metadata`. """ return response, metadata def pre_predict(self, request: prediction_service.PredictRequest, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[prediction_service.PredictRequest, Sequence[Tuple[str, Union[str, bytes]]]]: """Pre-rpc interceptor for predict Override in a subclass to manipulate the request or metadata before they are sent to the PredictionService server. """ return request, metadata def post_predict(self, response: prediction_service.PredictResponse) -> prediction_service.PredictResponse: """Post-rpc interceptor for predict DEPRECATED. Please use the `post_predict_with_metadata` interceptor instead. Override in a subclass to read or manipulate the response after it is returned by the PredictionService server but before it is returned to user code. This `post_predict` interceptor runs before the `post_predict_with_metadata` interceptor. """ return response def post_predict_with_metadata(self, response: prediction_service.PredictResponse, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[prediction_service.PredictResponse, Sequence[Tuple[str, Union[str, bytes]]]]: """Post-rpc interceptor for predict Override in a subclass to read or manipulate the response or metadata after it is returned by the PredictionService server but before it is returned to user code. We recommend only using this `post_predict_with_metadata` interceptor in new development instead of the `post_predict` interceptor. When both interceptors are used, this `post_predict_with_metadata` interceptor runs after the `post_predict` interceptor. The (possibly modified) response returned by `post_predict` will be passed to `post_predict_with_metadata`. """ return response, metadata def pre_predict_long_running(self, request: prediction_service.PredictLongRunningRequest, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[prediction_service.PredictLongRunningRequest, Sequence[Tuple[str, Union[str, bytes]]]]: """Pre-rpc interceptor for predict_long_running Override in a subclass to manipulate the request or metadata before they are sent to the PredictionService server. """ return request, metadata def post_predict_long_running(self, response: operations_pb2.Operation) -> operations_pb2.Operation: """Post-rpc interceptor for predict_long_running DEPRECATED. Please use the `post_predict_long_running_with_metadata` interceptor instead. Override in a subclass to read or manipulate the response after it is returned by the PredictionService server but before it is returned to user code. This `post_predict_long_running` interceptor runs before the `post_predict_long_running_with_metadata` interceptor. """ return response def post_predict_long_running_with_metadata(self, response: operations_pb2.Operation, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[operations_pb2.Operation, Sequence[Tuple[str, Union[str, bytes]]]]: """Post-rpc interceptor for predict_long_running Override in a subclass to read or manipulate the response or metadata after it is returned by the PredictionService server but before it is returned to user code. We recommend only using this `post_predict_long_running_with_metadata` interceptor in new development instead of the `post_predict_long_running` interceptor. When both interceptors are used, this `post_predict_long_running_with_metadata` interceptor runs after the `post_predict_long_running` interceptor. The (possibly modified) response returned by `post_predict_long_running` will be passed to `post_predict_long_running_with_metadata`. """ return response, metadata def pre_raw_predict(self, request: prediction_service.RawPredictRequest, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[prediction_service.RawPredictRequest, Sequence[Tuple[str, Union[str, bytes]]]]: """Pre-rpc interceptor for raw_predict Override in a subclass to manipulate the request or metadata before they are sent to the PredictionService server. """ return request, metadata def post_raw_predict(self, response: httpbody_pb2.HttpBody) -> httpbody_pb2.HttpBody: """Post-rpc interceptor for raw_predict DEPRECATED. Please use the `post_raw_predict_with_metadata` interceptor instead. Override in a subclass to read or manipulate the response after it is returned by the PredictionService server but before it is returned to user code. This `post_raw_predict` interceptor runs before the `post_raw_predict_with_metadata` interceptor. """ return response def post_raw_predict_with_metadata(self, response: httpbody_pb2.HttpBody, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[httpbody_pb2.HttpBody, Sequence[Tuple[str, Union[str, bytes]]]]: """Post-rpc interceptor for raw_predict Override in a subclass to read or manipulate the response or metadata after it is returned by the PredictionService server but before it is returned to user code. We recommend only using this `post_raw_predict_with_metadata` interceptor in new development instead of the `post_raw_predict` interceptor. When both interceptors are used, this `post_raw_predict_with_metadata` interceptor runs after the `post_raw_predict` interceptor. The (possibly modified) response returned by `post_raw_predict` will be passed to `post_raw_predict_with_metadata`. """ return response, metadata def pre_server_streaming_predict(self, request: prediction_service.StreamingPredictRequest, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[prediction_service.StreamingPredictRequest, Sequence[Tuple[str, Union[str, bytes]]]]: """Pre-rpc interceptor for server_streaming_predict Override in a subclass to manipulate the request or metadata before they are sent to the PredictionService server. """ return request, metadata def post_server_streaming_predict(self, response: rest_streaming.ResponseIterator) -> rest_streaming.ResponseIterator: """Post-rpc interceptor for server_streaming_predict DEPRECATED. Please use the `post_server_streaming_predict_with_metadata` interceptor instead. Override in a subclass to read or manipulate the response after it is returned by the PredictionService server but before it is returned to user code. This `post_server_streaming_predict` interceptor runs before the `post_server_streaming_predict_with_metadata` interceptor. """ return response def post_server_streaming_predict_with_metadata(self, response: rest_streaming.ResponseIterator, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[rest_streaming.ResponseIterator, Sequence[Tuple[str, Union[str, bytes]]]]: """Post-rpc interceptor for server_streaming_predict Override in a subclass to read or manipulate the response or metadata after it is returned by the PredictionService server but before it is returned to user code. We recommend only using this `post_server_streaming_predict_with_metadata` interceptor in new development instead of the `post_server_streaming_predict` interceptor. When both interceptors are used, this `post_server_streaming_predict_with_metadata` interceptor runs after the `post_server_streaming_predict` interceptor. The (possibly modified) response returned by `post_server_streaming_predict` will be passed to `post_server_streaming_predict_with_metadata`. """ return response, metadata def pre_stream_generate_content(self, request: prediction_service.GenerateContentRequest, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[prediction_service.GenerateContentRequest, Sequence[Tuple[str, Union[str, bytes]]]]: """Pre-rpc interceptor for stream_generate_content Override in a subclass to manipulate the request or metadata before they are sent to the PredictionService server. """ return request, metadata def post_stream_generate_content(self, response: rest_streaming.ResponseIterator) -> rest_streaming.ResponseIterator: """Post-rpc interceptor for stream_generate_content DEPRECATED. Please use the `post_stream_generate_content_with_metadata` interceptor instead. Override in a subclass to read or manipulate the response after it is returned by the PredictionService server but before it is returned to user code. This `post_stream_generate_content` interceptor runs before the `post_stream_generate_content_with_metadata` interceptor. """ return response def post_stream_generate_content_with_metadata(self, response: rest_streaming.ResponseIterator, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[rest_streaming.ResponseIterator, Sequence[Tuple[str, Union[str, bytes]]]]: """Post-rpc interceptor for stream_generate_content Override in a subclass to read or manipulate the response or metadata after it is returned by the PredictionService server but before it is returned to user code. We recommend only using this `post_stream_generate_content_with_metadata` interceptor in new development instead of the `post_stream_generate_content` interceptor. When both interceptors are used, this `post_stream_generate_content_with_metadata` interceptor runs after the `post_stream_generate_content` interceptor. The (possibly modified) response returned by `post_stream_generate_content` will be passed to `post_stream_generate_content_with_metadata`. """ return response, metadata def pre_stream_raw_predict(self, request: prediction_service.StreamRawPredictRequest, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[prediction_service.StreamRawPredictRequest, Sequence[Tuple[str, Union[str, bytes]]]]: """Pre-rpc interceptor for stream_raw_predict Override in a subclass to manipulate the request or metadata before they are sent to the PredictionService server. """ return request, metadata def post_stream_raw_predict(self, response: rest_streaming.ResponseIterator) -> rest_streaming.ResponseIterator: """Post-rpc interceptor for stream_raw_predict DEPRECATED. Please use the `post_stream_raw_predict_with_metadata` interceptor instead. Override in a subclass to read or manipulate the response after it is returned by the PredictionService server but before it is returned to user code. This `post_stream_raw_predict` interceptor runs before the `post_stream_raw_predict_with_metadata` interceptor. """ return response def post_stream_raw_predict_with_metadata(self, response: rest_streaming.ResponseIterator, metadata: Sequence[Tuple[str, Union[str, bytes]]]) -> Tuple[rest_streaming.ResponseIterator, Sequence[Tuple[str, Union[str, bytes]]]]: """Post-rpc interceptor for stream_raw_predict Override in a subclass to read or manipulate the response or metadata after it is returned by the PredictionService server but before it is returned to user code. We recommend only using this `post_stream_raw_predict_with_metadata` interceptor in new development instead of the `post_stream_raw_predict` interceptor. When both interceptors are used, this `post_stream_raw_predict_with_metadata` interceptor runs after the `post_stream_raw_predict` interceptor. The (possibly modified) response returned by `post_stream_raw_predict` will be passed to `post_stream_raw_predict_with_metadata`. """ return response, metadata @dataclasses.dataclass class PredictionServiceRestStub: _session: AuthorizedSession _host: str _interceptor: PredictionServiceRestInterceptor class PredictionServiceRestTransport(_BasePredictionServiceRestTransport): """REST backend synchronous 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 JSON representations of protocol buffers over HTTP/1.1 """ def __init__(self, *, host: str = 'aiplatform.googleapis.com', credentials: Optional[ga_credentials.Credentials] = None, credentials_file: Optional[str] = None, scopes: Optional[Sequence[str]] = 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, url_scheme: str = 'https', interceptor: Optional[PredictionServiceRestInterceptor] = None, api_audience: Optional[str] = None, ) -> None: """Instantiate the transport. NOTE: This REST transport functionality is currently in a beta state (preview). We welcome your feedback via a GitHub issue in this library's repository. Thank you! 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. 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 ``channel`` is provided. This argument will be removed in the next major version of this library. scopes (Optional(Sequence[str])): A list of scopes. This argument is ignored if ``channel`` is provided. client_cert_source_for_mtls (Callable[[], Tuple[bytes, bytes]]): Client certificate to configure mutual TLS HTTP channel. It is ignored if ``channel`` 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 are developing your own client library. always_use_jwt_access (Optional[bool]): Whether self signed JWT should be used for service account credentials. url_scheme: the protocol scheme for the API endpoint. Normally "https", but for testing or local servers, "http" can be specified. """ # Run the base constructor # TODO(yon-mg): resolve other ctor params i.e. scopes, quota, etc. # TODO: When custom host (api_endpoint) is set, `scopes` must *also* be set on the # credentials object super().__init__( host=host, credentials=credentials, client_info=client_info, always_use_jwt_access=always_use_jwt_access, url_scheme=url_scheme, api_audience=api_audience ) self._session = AuthorizedSession( self._credentials, default_host=self.DEFAULT_HOST) if client_cert_source_for_mtls: self._session.configure_mtls_channel(client_cert_source_for_mtls) self._interceptor = interceptor or PredictionServiceRestInterceptor() self._prep_wrapped_messages(client_info) class _ChatCompletions(_BasePredictionServiceRestTransport._BaseChatCompletions, PredictionServiceRestStub): def __hash__(self): return hash("PredictionServiceRestTransport.ChatCompletions") @staticmethod def _get_response( host, metadata, query_params, session, timeout, transcoded_request, body=None): uri = transcoded_request['uri'] method = transcoded_request['method'] headers = dict(metadata) headers['Content-Type'] = 'application/json' response = getattr(session, method)( "{host}{uri}".format(host=host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params, strict=True), data=body, stream=True, ) return response def __call__(self, request: prediction_service.ChatCompletionsRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: Optional[float]=None, metadata: Sequence[Tuple[str, Union[str, bytes]]]=(), ) -> rest_streaming.ResponseIterator: r"""Call the chat completions method over HTTP. Args: request (~.prediction_service.ChatCompletionsRequest): The request object. Request message for [PredictionService.ChatCompletions] 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: ~.httpbody_pb2.HttpBody: Message that represents an arbitrary HTTP body. It should only be used for payload formats that can't be represented as JSON, such as raw binary or an HTML page. This message can be used both in streaming and non-streaming API methods in the request as well as the response. It can be used as a top-level request field, which is convenient if one wants to extract parameters from either the URL or HTTP template into the request fields and also want access to the raw HTTP body. Example: :: message GetResourceRequest { // A unique request id. string request_id = 1; // The raw HTTP body is bound to this field. google.api.HttpBody http_body = 2; } service ResourceService { rpc GetResource(GetResourceRequest) returns (google.api.HttpBody); rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty); } Example with streaming methods: :: service CaldavService { rpc GetCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); rpc UpdateCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); } Use of this type only changes how the request and response bodies are handled, all other features will continue to work unchanged. """ http_options = _BasePredictionServiceRestTransport._BaseChatCompletions._get_http_options() request, metadata = self._interceptor.pre_chat_completions(request, metadata) transcoded_request = _BasePredictionServiceRestTransport._BaseChatCompletions._get_transcoded_request(http_options, request) body = _BasePredictionServiceRestTransport._BaseChatCompletions._get_request_body_json(transcoded_request) # Jsonify the query params query_params = _BasePredictionServiceRestTransport._BaseChatCompletions._get_query_params_json(transcoded_request) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER request_url = "{host}{uri}".format(host=self._host, uri=transcoded_request['uri']) method = transcoded_request['method'] try: request_payload = json_format.MessageToJson(request) except: request_payload = None http_request = { "payload": request_payload, "requestMethod": method, "requestUrl": request_url, "headers": dict(metadata), } _LOGGER.debug( f"Sending request for google.cloud.aiplatform_v1beta1.PredictionServiceClient.ChatCompletions", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "ChatCompletions", "httpRequest": http_request, "metadata": http_request["headers"], }, ) # Send the request response = PredictionServiceRestTransport._ChatCompletions._get_response(self._host, metadata, query_params, self._session, timeout, transcoded_request, body) # In case of error, raise the appropriate core_exceptions.GoogleAPICallError exception # subclass. if response.status_code >= 400: raise core_exceptions.from_http_response(response) # Return the response resp = rest_streaming.ResponseIterator(response, httpbody_pb2.HttpBody) resp = self._interceptor.post_chat_completions(resp) response_metadata = [(k, str(v)) for k, v in response.headers.items()] resp, _ = self._interceptor.post_chat_completions_with_metadata(resp, response_metadata) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER http_response = { "headers": dict(response.headers), "status": response.status_code, } _LOGGER.debug( "Received response for google.cloud.aiplatform_v1beta1.PredictionServiceClient.chat_completions", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "ChatCompletions", "metadata": http_response["headers"], "httpResponse": http_response, }, ) return resp class _CountTokens(_BasePredictionServiceRestTransport._BaseCountTokens, PredictionServiceRestStub): def __hash__(self): return hash("PredictionServiceRestTransport.CountTokens") @staticmethod def _get_response( host, metadata, query_params, session, timeout, transcoded_request, body=None): uri = transcoded_request['uri'] method = transcoded_request['method'] headers = dict(metadata) headers['Content-Type'] = 'application/json' response = getattr(session, method)( "{host}{uri}".format(host=host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params, strict=True), data=body, ) return response def __call__(self, request: prediction_service.CountTokensRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: Optional[float]=None, metadata: Sequence[Tuple[str, Union[str, bytes]]]=(), ) -> prediction_service.CountTokensResponse: r"""Call the count tokens method over HTTP. Args: request (~.prediction_service.CountTokensRequest): The request object. Request message for [PredictionService.CountTokens][google.cloud.aiplatform.v1beta1.PredictionService.CountTokens]. 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: ~.prediction_service.CountTokensResponse: Response message for [PredictionService.CountTokens][google.cloud.aiplatform.v1beta1.PredictionService.CountTokens]. """ http_options = _BasePredictionServiceRestTransport._BaseCountTokens._get_http_options() request, metadata = self._interceptor.pre_count_tokens(request, metadata) transcoded_request = _BasePredictionServiceRestTransport._BaseCountTokens._get_transcoded_request(http_options, request) body = _BasePredictionServiceRestTransport._BaseCountTokens._get_request_body_json(transcoded_request) # Jsonify the query params query_params = _BasePredictionServiceRestTransport._BaseCountTokens._get_query_params_json(transcoded_request) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER request_url = "{host}{uri}".format(host=self._host, uri=transcoded_request['uri']) method = transcoded_request['method'] try: request_payload = type(request).to_json(request) except: request_payload = None http_request = { "payload": request_payload, "requestMethod": method, "requestUrl": request_url, "headers": dict(metadata), } _LOGGER.debug( f"Sending request for google.cloud.aiplatform_v1beta1.PredictionServiceClient.CountTokens", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "CountTokens", "httpRequest": http_request, "metadata": http_request["headers"], }, ) # Send the request response = PredictionServiceRestTransport._CountTokens._get_response(self._host, metadata, query_params, self._session, timeout, transcoded_request, body) # In case of error, raise the appropriate core_exceptions.GoogleAPICallError exception # subclass. if response.status_code >= 400: raise core_exceptions.from_http_response(response) # Return the response resp = prediction_service.CountTokensResponse() pb_resp = prediction_service.CountTokensResponse.pb(resp) json_format.Parse(response.content, pb_resp, ignore_unknown_fields=True) resp = self._interceptor.post_count_tokens(resp) response_metadata = [(k, str(v)) for k, v in response.headers.items()] resp, _ = self._interceptor.post_count_tokens_with_metadata(resp, response_metadata) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER try: response_payload = prediction_service.CountTokensResponse.to_json(response) except: response_payload = None http_response = { "payload": response_payload, "headers": dict(response.headers), "status": response.status_code, } _LOGGER.debug( "Received response for google.cloud.aiplatform_v1beta1.PredictionServiceClient.count_tokens", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "CountTokens", "metadata": http_response["headers"], "httpResponse": http_response, }, ) return resp class _DirectPredict(_BasePredictionServiceRestTransport._BaseDirectPredict, PredictionServiceRestStub): def __hash__(self): return hash("PredictionServiceRestTransport.DirectPredict") @staticmethod def _get_response( host, metadata, query_params, session, timeout, transcoded_request, body=None): uri = transcoded_request['uri'] method = transcoded_request['method'] headers = dict(metadata) headers['Content-Type'] = 'application/json' response = getattr(session, method)( "{host}{uri}".format(host=host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params, strict=True), data=body, ) return response def __call__(self, request: prediction_service.DirectPredictRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: Optional[float]=None, metadata: Sequence[Tuple[str, Union[str, bytes]]]=(), ) -> prediction_service.DirectPredictResponse: r"""Call the direct predict method over HTTP. Args: request (~.prediction_service.DirectPredictRequest): The request object. Request message for [PredictionService.DirectPredict][google.cloud.aiplatform.v1beta1.PredictionService.DirectPredict]. 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: ~.prediction_service.DirectPredictResponse: Response message for [PredictionService.DirectPredict][google.cloud.aiplatform.v1beta1.PredictionService.DirectPredict]. """ http_options = _BasePredictionServiceRestTransport._BaseDirectPredict._get_http_options() request, metadata = self._interceptor.pre_direct_predict(request, metadata) transcoded_request = _BasePredictionServiceRestTransport._BaseDirectPredict._get_transcoded_request(http_options, request) body = _BasePredictionServiceRestTransport._BaseDirectPredict._get_request_body_json(transcoded_request) # Jsonify the query params query_params = _BasePredictionServiceRestTransport._BaseDirectPredict._get_query_params_json(transcoded_request) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER request_url = "{host}{uri}".format(host=self._host, uri=transcoded_request['uri']) method = transcoded_request['method'] try: request_payload = type(request).to_json(request) except: request_payload = None http_request = { "payload": request_payload, "requestMethod": method, "requestUrl": request_url, "headers": dict(metadata), } _LOGGER.debug( f"Sending request for google.cloud.aiplatform_v1beta1.PredictionServiceClient.DirectPredict", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "DirectPredict", "httpRequest": http_request, "metadata": http_request["headers"], }, ) # Send the request response = PredictionServiceRestTransport._DirectPredict._get_response(self._host, metadata, query_params, self._session, timeout, transcoded_request, body) # In case of error, raise the appropriate core_exceptions.GoogleAPICallError exception # subclass. if response.status_code >= 400: raise core_exceptions.from_http_response(response) # Return the response resp = prediction_service.DirectPredictResponse() pb_resp = prediction_service.DirectPredictResponse.pb(resp) json_format.Parse(response.content, pb_resp, ignore_unknown_fields=True) resp = self._interceptor.post_direct_predict(resp) response_metadata = [(k, str(v)) for k, v in response.headers.items()] resp, _ = self._interceptor.post_direct_predict_with_metadata(resp, response_metadata) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER try: response_payload = prediction_service.DirectPredictResponse.to_json(response) except: response_payload = None http_response = { "payload": response_payload, "headers": dict(response.headers), "status": response.status_code, } _LOGGER.debug( "Received response for google.cloud.aiplatform_v1beta1.PredictionServiceClient.direct_predict", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "DirectPredict", "metadata": http_response["headers"], "httpResponse": http_response, }, ) return resp class _DirectRawPredict(_BasePredictionServiceRestTransport._BaseDirectRawPredict, PredictionServiceRestStub): def __hash__(self): return hash("PredictionServiceRestTransport.DirectRawPredict") @staticmethod def _get_response( host, metadata, query_params, session, timeout, transcoded_request, body=None): uri = transcoded_request['uri'] method = transcoded_request['method'] headers = dict(metadata) headers['Content-Type'] = 'application/json' response = getattr(session, method)( "{host}{uri}".format(host=host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params, strict=True), data=body, ) return response def __call__(self, request: prediction_service.DirectRawPredictRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: Optional[float]=None, metadata: Sequence[Tuple[str, Union[str, bytes]]]=(), ) -> prediction_service.DirectRawPredictResponse: r"""Call the direct raw predict method over HTTP. Args: request (~.prediction_service.DirectRawPredictRequest): The request object. Request message for [PredictionService.DirectRawPredict][google.cloud.aiplatform.v1beta1.PredictionService.DirectRawPredict]. 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: ~.prediction_service.DirectRawPredictResponse: Response message for [PredictionService.DirectRawPredict][google.cloud.aiplatform.v1beta1.PredictionService.DirectRawPredict]. """ http_options = _BasePredictionServiceRestTransport._BaseDirectRawPredict._get_http_options() request, metadata = self._interceptor.pre_direct_raw_predict(request, metadata) transcoded_request = _BasePredictionServiceRestTransport._BaseDirectRawPredict._get_transcoded_request(http_options, request) body = _BasePredictionServiceRestTransport._BaseDirectRawPredict._get_request_body_json(transcoded_request) # Jsonify the query params query_params = _BasePredictionServiceRestTransport._BaseDirectRawPredict._get_query_params_json(transcoded_request) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER request_url = "{host}{uri}".format(host=self._host, uri=transcoded_request['uri']) method = transcoded_request['method'] try: request_payload = type(request).to_json(request) except: request_payload = None http_request = { "payload": request_payload, "requestMethod": method, "requestUrl": request_url, "headers": dict(metadata), } _LOGGER.debug( f"Sending request for google.cloud.aiplatform_v1beta1.PredictionServiceClient.DirectRawPredict", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "DirectRawPredict", "httpRequest": http_request, "metadata": http_request["headers"], }, ) # Send the request response = PredictionServiceRestTransport._DirectRawPredict._get_response(self._host, metadata, query_params, self._session, timeout, transcoded_request, body) # In case of error, raise the appropriate core_exceptions.GoogleAPICallError exception # subclass. if response.status_code >= 400: raise core_exceptions.from_http_response(response) # Return the response resp = prediction_service.DirectRawPredictResponse() pb_resp = prediction_service.DirectRawPredictResponse.pb(resp) json_format.Parse(response.content, pb_resp, ignore_unknown_fields=True) resp = self._interceptor.post_direct_raw_predict(resp) response_metadata = [(k, str(v)) for k, v in response.headers.items()] resp, _ = self._interceptor.post_direct_raw_predict_with_metadata(resp, response_metadata) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER try: response_payload = prediction_service.DirectRawPredictResponse.to_json(response) except: response_payload = None http_response = { "payload": response_payload, "headers": dict(response.headers), "status": response.status_code, } _LOGGER.debug( "Received response for google.cloud.aiplatform_v1beta1.PredictionServiceClient.direct_raw_predict", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "DirectRawPredict", "metadata": http_response["headers"], "httpResponse": http_response, }, ) return resp class _EmbedContent(_BasePredictionServiceRestTransport._BaseEmbedContent, PredictionServiceRestStub): def __hash__(self): return hash("PredictionServiceRestTransport.EmbedContent") @staticmethod def _get_response( host, metadata, query_params, session, timeout, transcoded_request, body=None): uri = transcoded_request['uri'] method = transcoded_request['method'] headers = dict(metadata) headers['Content-Type'] = 'application/json' response = getattr(session, method)( "{host}{uri}".format(host=host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params, strict=True), data=body, ) return response def __call__(self, request: prediction_service.EmbedContentRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: Optional[float]=None, metadata: Sequence[Tuple[str, Union[str, bytes]]]=(), ) -> prediction_service.EmbedContentResponse: r"""Call the embed content method over HTTP. Args: request (~.prediction_service.EmbedContentRequest): The request object. Request message for [PredictionService.EmbedContent][google.cloud.aiplatform.v1beta1.PredictionService.EmbedContent]. 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: ~.prediction_service.EmbedContentResponse: Response message for [PredictionService.EmbedContent][google.cloud.aiplatform.v1beta1.PredictionService.EmbedContent]. """ http_options = _BasePredictionServiceRestTransport._BaseEmbedContent._get_http_options() request, metadata = self._interceptor.pre_embed_content(request, metadata) transcoded_request = _BasePredictionServiceRestTransport._BaseEmbedContent._get_transcoded_request(http_options, request) body = _BasePredictionServiceRestTransport._BaseEmbedContent._get_request_body_json(transcoded_request) # Jsonify the query params query_params = _BasePredictionServiceRestTransport._BaseEmbedContent._get_query_params_json(transcoded_request) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER request_url = "{host}{uri}".format(host=self._host, uri=transcoded_request['uri']) method = transcoded_request['method'] try: request_payload = type(request).to_json(request) except: request_payload = None http_request = { "payload": request_payload, "requestMethod": method, "requestUrl": request_url, "headers": dict(metadata), } _LOGGER.debug( f"Sending request for google.cloud.aiplatform_v1beta1.PredictionServiceClient.EmbedContent", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "EmbedContent", "httpRequest": http_request, "metadata": http_request["headers"], }, ) # Send the request response = PredictionServiceRestTransport._EmbedContent._get_response(self._host, metadata, query_params, self._session, timeout, transcoded_request, body) # In case of error, raise the appropriate core_exceptions.GoogleAPICallError exception # subclass. if response.status_code >= 400: raise core_exceptions.from_http_response(response) # Return the response resp = prediction_service.EmbedContentResponse() pb_resp = prediction_service.EmbedContentResponse.pb(resp) json_format.Parse(response.content, pb_resp, ignore_unknown_fields=True) resp = self._interceptor.post_embed_content(resp) response_metadata = [(k, str(v)) for k, v in response.headers.items()] resp, _ = self._interceptor.post_embed_content_with_metadata(resp, response_metadata) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER try: response_payload = prediction_service.EmbedContentResponse.to_json(response) except: response_payload = None http_response = { "payload": response_payload, "headers": dict(response.headers), "status": response.status_code, } _LOGGER.debug( "Received response for google.cloud.aiplatform_v1beta1.PredictionServiceClient.embed_content", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "EmbedContent", "metadata": http_response["headers"], "httpResponse": http_response, }, ) return resp class _Explain(_BasePredictionServiceRestTransport._BaseExplain, PredictionServiceRestStub): def __hash__(self): return hash("PredictionServiceRestTransport.Explain") @staticmethod def _get_response( host, metadata, query_params, session, timeout, transcoded_request, body=None): uri = transcoded_request['uri'] method = transcoded_request['method'] headers = dict(metadata) headers['Content-Type'] = 'application/json' response = getattr(session, method)( "{host}{uri}".format(host=host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params, strict=True), data=body, ) return response def __call__(self, request: prediction_service.ExplainRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: Optional[float]=None, metadata: Sequence[Tuple[str, Union[str, bytes]]]=(), ) -> prediction_service.ExplainResponse: r"""Call the explain method over HTTP. Args: request (~.prediction_service.ExplainRequest): The request object. Request message for [PredictionService.Explain][google.cloud.aiplatform.v1beta1.PredictionService.Explain]. 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: ~.prediction_service.ExplainResponse: Response message for [PredictionService.Explain][google.cloud.aiplatform.v1beta1.PredictionService.Explain]. """ http_options = _BasePredictionServiceRestTransport._BaseExplain._get_http_options() request, metadata = self._interceptor.pre_explain(request, metadata) transcoded_request = _BasePredictionServiceRestTransport._BaseExplain._get_transcoded_request(http_options, request) body = _BasePredictionServiceRestTransport._BaseExplain._get_request_body_json(transcoded_request) # Jsonify the query params query_params = _BasePredictionServiceRestTransport._BaseExplain._get_query_params_json(transcoded_request) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER request_url = "{host}{uri}".format(host=self._host, uri=transcoded_request['uri']) method = transcoded_request['method'] try: request_payload = type(request).to_json(request) except: request_payload = None http_request = { "payload": request_payload, "requestMethod": method, "requestUrl": request_url, "headers": dict(metadata), } _LOGGER.debug( f"Sending request for google.cloud.aiplatform_v1beta1.PredictionServiceClient.Explain", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "Explain", "httpRequest": http_request, "metadata": http_request["headers"], }, ) # Send the request response = PredictionServiceRestTransport._Explain._get_response(self._host, metadata, query_params, self._session, timeout, transcoded_request, body) # In case of error, raise the appropriate core_exceptions.GoogleAPICallError exception # subclass. if response.status_code >= 400: raise core_exceptions.from_http_response(response) # Return the response resp = prediction_service.ExplainResponse() pb_resp = prediction_service.ExplainResponse.pb(resp) json_format.Parse(response.content, pb_resp, ignore_unknown_fields=True) resp = self._interceptor.post_explain(resp) response_metadata = [(k, str(v)) for k, v in response.headers.items()] resp, _ = self._interceptor.post_explain_with_metadata(resp, response_metadata) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER try: response_payload = prediction_service.ExplainResponse.to_json(response) except: response_payload = None http_response = { "payload": response_payload, "headers": dict(response.headers), "status": response.status_code, } _LOGGER.debug( "Received response for google.cloud.aiplatform_v1beta1.PredictionServiceClient.explain", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "Explain", "metadata": http_response["headers"], "httpResponse": http_response, }, ) return resp class _FetchPredictOperation(_BasePredictionServiceRestTransport._BaseFetchPredictOperation, PredictionServiceRestStub): def __hash__(self): return hash("PredictionServiceRestTransport.FetchPredictOperation") @staticmethod def _get_response( host, metadata, query_params, session, timeout, transcoded_request, body=None): uri = transcoded_request['uri'] method = transcoded_request['method'] headers = dict(metadata) headers['Content-Type'] = 'application/json' response = getattr(session, method)( "{host}{uri}".format(host=host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params, strict=True), data=body, ) return response def __call__(self, request: prediction_service.FetchPredictOperationRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: Optional[float]=None, metadata: Sequence[Tuple[str, Union[str, bytes]]]=(), ) -> operations_pb2.Operation: r"""Call the fetch predict operation method over HTTP. Args: request (~.prediction_service.FetchPredictOperationRequest): The request object. Request message for [PredictionService.FetchPredictOperation][google.cloud.aiplatform.v1beta1.PredictionService.FetchPredictOperation]. 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: This resource represents a long-running operation that is the result of a network API call. """ http_options = _BasePredictionServiceRestTransport._BaseFetchPredictOperation._get_http_options() request, metadata = self._interceptor.pre_fetch_predict_operation(request, metadata) transcoded_request = _BasePredictionServiceRestTransport._BaseFetchPredictOperation._get_transcoded_request(http_options, request) body = _BasePredictionServiceRestTransport._BaseFetchPredictOperation._get_request_body_json(transcoded_request) # Jsonify the query params query_params = _BasePredictionServiceRestTransport._BaseFetchPredictOperation._get_query_params_json(transcoded_request) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER request_url = "{host}{uri}".format(host=self._host, uri=transcoded_request['uri']) method = transcoded_request['method'] try: request_payload = json_format.MessageToJson(request) except: request_payload = None http_request = { "payload": request_payload, "requestMethod": method, "requestUrl": request_url, "headers": dict(metadata), } _LOGGER.debug( f"Sending request for google.cloud.aiplatform_v1beta1.PredictionServiceClient.FetchPredictOperation", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "FetchPredictOperation", "httpRequest": http_request, "metadata": http_request["headers"], }, ) # Send the request response = PredictionServiceRestTransport._FetchPredictOperation._get_response(self._host, metadata, query_params, self._session, timeout, transcoded_request, body) # In case of error, raise the appropriate core_exceptions.GoogleAPICallError exception # subclass. if response.status_code >= 400: raise core_exceptions.from_http_response(response) # Return the response resp = operations_pb2.Operation() pb_resp = resp json_format.Parse(response.content, pb_resp, ignore_unknown_fields=True) resp = self._interceptor.post_fetch_predict_operation(resp) response_metadata = [(k, str(v)) for k, v in response.headers.items()] resp, _ = self._interceptor.post_fetch_predict_operation_with_metadata(resp, response_metadata) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER try: response_payload = json_format.MessageToJson(resp) except: response_payload = None http_response = { "payload": response_payload, "headers": dict(response.headers), "status": response.status_code, } _LOGGER.debug( "Received response for google.cloud.aiplatform_v1beta1.PredictionServiceClient.fetch_predict_operation", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "FetchPredictOperation", "metadata": http_response["headers"], "httpResponse": http_response, }, ) return resp class _GenerateContent(_BasePredictionServiceRestTransport._BaseGenerateContent, PredictionServiceRestStub): def __hash__(self): return hash("PredictionServiceRestTransport.GenerateContent") @staticmethod def _get_response( host, metadata, query_params, session, timeout, transcoded_request, body=None): uri = transcoded_request['uri'] method = transcoded_request['method'] headers = dict(metadata) headers['Content-Type'] = 'application/json' response = getattr(session, method)( "{host}{uri}".format(host=host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params, strict=True), data=body, ) return response def __call__(self, request: prediction_service.GenerateContentRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: Optional[float]=None, metadata: Sequence[Tuple[str, Union[str, bytes]]]=(), ) -> prediction_service.GenerateContentResponse: r"""Call the generate content method over HTTP. Args: request (~.prediction_service.GenerateContentRequest): The request object. Request message for [PredictionService.GenerateContent]. 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: ~.prediction_service.GenerateContentResponse: Response message for [PredictionService.GenerateContent]. """ http_options = _BasePredictionServiceRestTransport._BaseGenerateContent._get_http_options() request, metadata = self._interceptor.pre_generate_content(request, metadata) transcoded_request = _BasePredictionServiceRestTransport._BaseGenerateContent._get_transcoded_request(http_options, request) body = _BasePredictionServiceRestTransport._BaseGenerateContent._get_request_body_json(transcoded_request) # Jsonify the query params query_params = _BasePredictionServiceRestTransport._BaseGenerateContent._get_query_params_json(transcoded_request) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER request_url = "{host}{uri}".format(host=self._host, uri=transcoded_request['uri']) method = transcoded_request['method'] try: request_payload = type(request).to_json(request) except: request_payload = None http_request = { "payload": request_payload, "requestMethod": method, "requestUrl": request_url, "headers": dict(metadata), } _LOGGER.debug( f"Sending request for google.cloud.aiplatform_v1beta1.PredictionServiceClient.GenerateContent", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "GenerateContent", "httpRequest": http_request, "metadata": http_request["headers"], }, ) # Send the request response = PredictionServiceRestTransport._GenerateContent._get_response(self._host, metadata, query_params, self._session, timeout, transcoded_request, body) # In case of error, raise the appropriate core_exceptions.GoogleAPICallError exception # subclass. if response.status_code >= 400: raise core_exceptions.from_http_response(response) # Return the response resp = prediction_service.GenerateContentResponse() pb_resp = prediction_service.GenerateContentResponse.pb(resp) json_format.Parse(response.content, pb_resp, ignore_unknown_fields=True) resp = self._interceptor.post_generate_content(resp) response_metadata = [(k, str(v)) for k, v in response.headers.items()] resp, _ = self._interceptor.post_generate_content_with_metadata(resp, response_metadata) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER try: response_payload = prediction_service.GenerateContentResponse.to_json(response) except: response_payload = None http_response = { "payload": response_payload, "headers": dict(response.headers), "status": response.status_code, } _LOGGER.debug( "Received response for google.cloud.aiplatform_v1beta1.PredictionServiceClient.generate_content", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "GenerateContent", "metadata": http_response["headers"], "httpResponse": http_response, }, ) return resp class _Invoke(_BasePredictionServiceRestTransport._BaseInvoke, PredictionServiceRestStub): def __hash__(self): return hash("PredictionServiceRestTransport.Invoke") @staticmethod def _get_response( host, metadata, query_params, session, timeout, transcoded_request, body=None): uri = transcoded_request['uri'] method = transcoded_request['method'] headers = dict(metadata) headers['Content-Type'] = 'application/json' response = getattr(session, method)( "{host}{uri}".format(host=host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params, strict=True), data=body, stream=True, ) return response def __call__(self, request: prediction_service.InvokeRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: Optional[float]=None, metadata: Sequence[Tuple[str, Union[str, bytes]]]=(), ) -> rest_streaming.ResponseIterator: r"""Call the invoke method over HTTP. Args: request (~.prediction_service.InvokeRequest): The request object. Request message for [PredictionService.Invoke][google.cloud.aiplatform.v1beta1.PredictionService.Invoke]. 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: ~.httpbody_pb2.HttpBody: Message that represents an arbitrary HTTP body. It should only be used for payload formats that can't be represented as JSON, such as raw binary or an HTML page. This message can be used both in streaming and non-streaming API methods in the request as well as the response. It can be used as a top-level request field, which is convenient if one wants to extract parameters from either the URL or HTTP template into the request fields and also want access to the raw HTTP body. Example: :: message GetResourceRequest { // A unique request id. string request_id = 1; // The raw HTTP body is bound to this field. google.api.HttpBody http_body = 2; } service ResourceService { rpc GetResource(GetResourceRequest) returns (google.api.HttpBody); rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty); } Example with streaming methods: :: service CaldavService { rpc GetCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); rpc UpdateCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); } Use of this type only changes how the request and response bodies are handled, all other features will continue to work unchanged. """ http_options = _BasePredictionServiceRestTransport._BaseInvoke._get_http_options() request, metadata = self._interceptor.pre_invoke(request, metadata) transcoded_request = _BasePredictionServiceRestTransport._BaseInvoke._get_transcoded_request(http_options, request) body = _BasePredictionServiceRestTransport._BaseInvoke._get_request_body_json(transcoded_request) # Jsonify the query params query_params = _BasePredictionServiceRestTransport._BaseInvoke._get_query_params_json(transcoded_request) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER request_url = "{host}{uri}".format(host=self._host, uri=transcoded_request['uri']) method = transcoded_request['method'] try: request_payload = json_format.MessageToJson(request) except: request_payload = None http_request = { "payload": request_payload, "requestMethod": method, "requestUrl": request_url, "headers": dict(metadata), } _LOGGER.debug( f"Sending request for google.cloud.aiplatform_v1beta1.PredictionServiceClient.Invoke", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "Invoke", "httpRequest": http_request, "metadata": http_request["headers"], }, ) # Send the request response = PredictionServiceRestTransport._Invoke._get_response(self._host, metadata, query_params, self._session, timeout, transcoded_request, body) # In case of error, raise the appropriate core_exceptions.GoogleAPICallError exception # subclass. if response.status_code >= 400: raise core_exceptions.from_http_response(response) # Return the response resp = rest_streaming.ResponseIterator(response, httpbody_pb2.HttpBody) resp = self._interceptor.post_invoke(resp) response_metadata = [(k, str(v)) for k, v in response.headers.items()] resp, _ = self._interceptor.post_invoke_with_metadata(resp, response_metadata) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER http_response = { "headers": dict(response.headers), "status": response.status_code, } _LOGGER.debug( "Received response for google.cloud.aiplatform_v1beta1.PredictionServiceClient.invoke", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "Invoke", "metadata": http_response["headers"], "httpResponse": http_response, }, ) return resp class _Predict(_BasePredictionServiceRestTransport._BasePredict, PredictionServiceRestStub): def __hash__(self): return hash("PredictionServiceRestTransport.Predict") @staticmethod def _get_response( host, metadata, query_params, session, timeout, transcoded_request, body=None): uri = transcoded_request['uri'] method = transcoded_request['method'] headers = dict(metadata) headers['Content-Type'] = 'application/json' response = getattr(session, method)( "{host}{uri}".format(host=host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params, strict=True), data=body, ) return response def __call__(self, request: prediction_service.PredictRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: Optional[float]=None, metadata: Sequence[Tuple[str, Union[str, bytes]]]=(), ) -> prediction_service.PredictResponse: r"""Call the predict method over HTTP. Args: request (~.prediction_service.PredictRequest): The request object. Request message for [PredictionService.Predict][google.cloud.aiplatform.v1beta1.PredictionService.Predict]. 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: ~.prediction_service.PredictResponse: Response message for [PredictionService.Predict][google.cloud.aiplatform.v1beta1.PredictionService.Predict]. """ http_options = _BasePredictionServiceRestTransport._BasePredict._get_http_options() request, metadata = self._interceptor.pre_predict(request, metadata) transcoded_request = _BasePredictionServiceRestTransport._BasePredict._get_transcoded_request(http_options, request) body = _BasePredictionServiceRestTransport._BasePredict._get_request_body_json(transcoded_request) # Jsonify the query params query_params = _BasePredictionServiceRestTransport._BasePredict._get_query_params_json(transcoded_request) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER request_url = "{host}{uri}".format(host=self._host, uri=transcoded_request['uri']) method = transcoded_request['method'] try: request_payload = type(request).to_json(request) except: request_payload = None http_request = { "payload": request_payload, "requestMethod": method, "requestUrl": request_url, "headers": dict(metadata), } _LOGGER.debug( f"Sending request for google.cloud.aiplatform_v1beta1.PredictionServiceClient.Predict", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "Predict", "httpRequest": http_request, "metadata": http_request["headers"], }, ) # Send the request response = PredictionServiceRestTransport._Predict._get_response(self._host, metadata, query_params, self._session, timeout, transcoded_request, body) # In case of error, raise the appropriate core_exceptions.GoogleAPICallError exception # subclass. if response.status_code >= 400: raise core_exceptions.from_http_response(response) # Return the response resp = prediction_service.PredictResponse() pb_resp = prediction_service.PredictResponse.pb(resp) json_format.Parse(response.content, pb_resp, ignore_unknown_fields=True) resp = self._interceptor.post_predict(resp) response_metadata = [(k, str(v)) for k, v in response.headers.items()] resp, _ = self._interceptor.post_predict_with_metadata(resp, response_metadata) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER try: response_payload = prediction_service.PredictResponse.to_json(response) except: response_payload = None http_response = { "payload": response_payload, "headers": dict(response.headers), "status": response.status_code, } _LOGGER.debug( "Received response for google.cloud.aiplatform_v1beta1.PredictionServiceClient.predict", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "Predict", "metadata": http_response["headers"], "httpResponse": http_response, }, ) return resp class _PredictLongRunning(_BasePredictionServiceRestTransport._BasePredictLongRunning, PredictionServiceRestStub): def __hash__(self): return hash("PredictionServiceRestTransport.PredictLongRunning") @staticmethod def _get_response( host, metadata, query_params, session, timeout, transcoded_request, body=None): uri = transcoded_request['uri'] method = transcoded_request['method'] headers = dict(metadata) headers['Content-Type'] = 'application/json' response = getattr(session, method)( "{host}{uri}".format(host=host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params, strict=True), data=body, ) return response def __call__(self, request: prediction_service.PredictLongRunningRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: Optional[float]=None, metadata: Sequence[Tuple[str, Union[str, bytes]]]=(), ) -> operations_pb2.Operation: r"""Call the predict long running method over HTTP. Args: request (~.prediction_service.PredictLongRunningRequest): The request object. Request message for [PredictionService.PredictLongRunning][google.cloud.aiplatform.v1beta1.PredictionService.PredictLongRunning]. 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: This resource represents a long-running operation that is the result of a network API call. """ http_options = _BasePredictionServiceRestTransport._BasePredictLongRunning._get_http_options() request, metadata = self._interceptor.pre_predict_long_running(request, metadata) transcoded_request = _BasePredictionServiceRestTransport._BasePredictLongRunning._get_transcoded_request(http_options, request) body = _BasePredictionServiceRestTransport._BasePredictLongRunning._get_request_body_json(transcoded_request) # Jsonify the query params query_params = _BasePredictionServiceRestTransport._BasePredictLongRunning._get_query_params_json(transcoded_request) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER request_url = "{host}{uri}".format(host=self._host, uri=transcoded_request['uri']) method = transcoded_request['method'] try: request_payload = json_format.MessageToJson(request) except: request_payload = None http_request = { "payload": request_payload, "requestMethod": method, "requestUrl": request_url, "headers": dict(metadata), } _LOGGER.debug( f"Sending request for google.cloud.aiplatform_v1beta1.PredictionServiceClient.PredictLongRunning", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "PredictLongRunning", "httpRequest": http_request, "metadata": http_request["headers"], }, ) # Send the request response = PredictionServiceRestTransport._PredictLongRunning._get_response(self._host, metadata, query_params, self._session, timeout, transcoded_request, body) # In case of error, raise the appropriate core_exceptions.GoogleAPICallError exception # subclass. if response.status_code >= 400: raise core_exceptions.from_http_response(response) # Return the response resp = operations_pb2.Operation() pb_resp = resp json_format.Parse(response.content, pb_resp, ignore_unknown_fields=True) resp = self._interceptor.post_predict_long_running(resp) response_metadata = [(k, str(v)) for k, v in response.headers.items()] resp, _ = self._interceptor.post_predict_long_running_with_metadata(resp, response_metadata) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER try: response_payload = json_format.MessageToJson(resp) except: response_payload = None http_response = { "payload": response_payload, "headers": dict(response.headers), "status": response.status_code, } _LOGGER.debug( "Received response for google.cloud.aiplatform_v1beta1.PredictionServiceClient.predict_long_running", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "PredictLongRunning", "metadata": http_response["headers"], "httpResponse": http_response, }, ) return resp class _RawPredict(_BasePredictionServiceRestTransport._BaseRawPredict, PredictionServiceRestStub): def __hash__(self): return hash("PredictionServiceRestTransport.RawPredict") @staticmethod def _get_response( host, metadata, query_params, session, timeout, transcoded_request, body=None): uri = transcoded_request['uri'] method = transcoded_request['method'] headers = dict(metadata) headers['Content-Type'] = 'application/json' response = getattr(session, method)( "{host}{uri}".format(host=host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params, strict=True), data=body, ) return response def __call__(self, request: prediction_service.RawPredictRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: Optional[float]=None, metadata: Sequence[Tuple[str, Union[str, bytes]]]=(), ) -> httpbody_pb2.HttpBody: r"""Call the raw predict method over HTTP. Args: request (~.prediction_service.RawPredictRequest): The request object. Request message for [PredictionService.RawPredict][google.cloud.aiplatform.v1beta1.PredictionService.RawPredict]. 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: ~.httpbody_pb2.HttpBody: Message that represents an arbitrary HTTP body. It should only be used for payload formats that can't be represented as JSON, such as raw binary or an HTML page. This message can be used both in streaming and non-streaming API methods in the request as well as the response. It can be used as a top-level request field, which is convenient if one wants to extract parameters from either the URL or HTTP template into the request fields and also want access to the raw HTTP body. Example: :: message GetResourceRequest { // A unique request id. string request_id = 1; // The raw HTTP body is bound to this field. google.api.HttpBody http_body = 2; } service ResourceService { rpc GetResource(GetResourceRequest) returns (google.api.HttpBody); rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty); } Example with streaming methods: :: service CaldavService { rpc GetCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); rpc UpdateCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); } Use of this type only changes how the request and response bodies are handled, all other features will continue to work unchanged. """ http_options = _BasePredictionServiceRestTransport._BaseRawPredict._get_http_options() request, metadata = self._interceptor.pre_raw_predict(request, metadata) transcoded_request = _BasePredictionServiceRestTransport._BaseRawPredict._get_transcoded_request(http_options, request) body = _BasePredictionServiceRestTransport._BaseRawPredict._get_request_body_json(transcoded_request) # Jsonify the query params query_params = _BasePredictionServiceRestTransport._BaseRawPredict._get_query_params_json(transcoded_request) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER request_url = "{host}{uri}".format(host=self._host, uri=transcoded_request['uri']) method = transcoded_request['method'] try: request_payload = json_format.MessageToJson(request) except: request_payload = None http_request = { "payload": request_payload, "requestMethod": method, "requestUrl": request_url, "headers": dict(metadata), } _LOGGER.debug( f"Sending request for google.cloud.aiplatform_v1beta1.PredictionServiceClient.RawPredict", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "RawPredict", "httpRequest": http_request, "metadata": http_request["headers"], }, ) # Send the request response = PredictionServiceRestTransport._RawPredict._get_response(self._host, metadata, query_params, self._session, timeout, transcoded_request, body) # In case of error, raise the appropriate core_exceptions.GoogleAPICallError exception # subclass. if response.status_code >= 400: raise core_exceptions.from_http_response(response) # Return the response resp = httpbody_pb2.HttpBody() pb_resp = resp json_format.Parse(response.content, pb_resp, ignore_unknown_fields=True) resp = self._interceptor.post_raw_predict(resp) response_metadata = [(k, str(v)) for k, v in response.headers.items()] resp, _ = self._interceptor.post_raw_predict_with_metadata(resp, response_metadata) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER try: response_payload = json_format.MessageToJson(resp) except: response_payload = None http_response = { "payload": response_payload, "headers": dict(response.headers), "status": response.status_code, } _LOGGER.debug( "Received response for google.cloud.aiplatform_v1beta1.PredictionServiceClient.raw_predict", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "RawPredict", "metadata": http_response["headers"], "httpResponse": http_response, }, ) return resp class _ServerStreamingPredict(_BasePredictionServiceRestTransport._BaseServerStreamingPredict, PredictionServiceRestStub): def __hash__(self): return hash("PredictionServiceRestTransport.ServerStreamingPredict") @staticmethod def _get_response( host, metadata, query_params, session, timeout, transcoded_request, body=None): uri = transcoded_request['uri'] method = transcoded_request['method'] headers = dict(metadata) headers['Content-Type'] = 'application/json' response = getattr(session, method)( "{host}{uri}".format(host=host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params, strict=True), data=body, stream=True, ) return response def __call__(self, request: prediction_service.StreamingPredictRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: Optional[float]=None, metadata: Sequence[Tuple[str, Union[str, bytes]]]=(), ) -> rest_streaming.ResponseIterator: r"""Call the server streaming predict method over HTTP. Args: request (~.prediction_service.StreamingPredictRequest): The request object. Request message for [PredictionService.StreamingPredict][google.cloud.aiplatform.v1beta1.PredictionService.StreamingPredict]. The first message must contain [endpoint][google.cloud.aiplatform.v1beta1.StreamingPredictRequest.endpoint] field and optionally [input][]. The subsequent messages must contain [input][]. 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: ~.prediction_service.StreamingPredictResponse: Response message for [PredictionService.StreamingPredict][google.cloud.aiplatform.v1beta1.PredictionService.StreamingPredict]. """ http_options = _BasePredictionServiceRestTransport._BaseServerStreamingPredict._get_http_options() request, metadata = self._interceptor.pre_server_streaming_predict(request, metadata) transcoded_request = _BasePredictionServiceRestTransport._BaseServerStreamingPredict._get_transcoded_request(http_options, request) body = _BasePredictionServiceRestTransport._BaseServerStreamingPredict._get_request_body_json(transcoded_request) # Jsonify the query params query_params = _BasePredictionServiceRestTransport._BaseServerStreamingPredict._get_query_params_json(transcoded_request) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER request_url = "{host}{uri}".format(host=self._host, uri=transcoded_request['uri']) method = transcoded_request['method'] try: request_payload = type(request).to_json(request) except: request_payload = None http_request = { "payload": request_payload, "requestMethod": method, "requestUrl": request_url, "headers": dict(metadata), } _LOGGER.debug( f"Sending request for google.cloud.aiplatform_v1beta1.PredictionServiceClient.ServerStreamingPredict", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "ServerStreamingPredict", "httpRequest": http_request, "metadata": http_request["headers"], }, ) # Send the request response = PredictionServiceRestTransport._ServerStreamingPredict._get_response(self._host, metadata, query_params, self._session, timeout, transcoded_request, body) # In case of error, raise the appropriate core_exceptions.GoogleAPICallError exception # subclass. if response.status_code >= 400: raise core_exceptions.from_http_response(response) # Return the response resp = rest_streaming.ResponseIterator(response, prediction_service.StreamingPredictResponse) resp = self._interceptor.post_server_streaming_predict(resp) response_metadata = [(k, str(v)) for k, v in response.headers.items()] resp, _ = self._interceptor.post_server_streaming_predict_with_metadata(resp, response_metadata) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER http_response = { "headers": dict(response.headers), "status": response.status_code, } _LOGGER.debug( "Received response for google.cloud.aiplatform_v1beta1.PredictionServiceClient.server_streaming_predict", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "ServerStreamingPredict", "metadata": http_response["headers"], "httpResponse": http_response, }, ) return resp class _StreamDirectPredict(_BasePredictionServiceRestTransport._BaseStreamDirectPredict, PredictionServiceRestStub): def __hash__(self): return hash("PredictionServiceRestTransport.StreamDirectPredict") def __call__(self, request: prediction_service.StreamDirectPredictRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: Optional[float]=None, metadata: Sequence[Tuple[str, Union[str, bytes]]]=(), ) -> rest_streaming.ResponseIterator: raise NotImplementedError( "Method StreamDirectPredict is not available over REST transport" ) class _StreamDirectRawPredict(_BasePredictionServiceRestTransport._BaseStreamDirectRawPredict, PredictionServiceRestStub): def __hash__(self): return hash("PredictionServiceRestTransport.StreamDirectRawPredict") def __call__(self, request: prediction_service.StreamDirectRawPredictRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: Optional[float]=None, metadata: Sequence[Tuple[str, Union[str, bytes]]]=(), ) -> rest_streaming.ResponseIterator: raise NotImplementedError( "Method StreamDirectRawPredict is not available over REST transport" ) class _StreamGenerateContent(_BasePredictionServiceRestTransport._BaseStreamGenerateContent, PredictionServiceRestStub): def __hash__(self): return hash("PredictionServiceRestTransport.StreamGenerateContent") @staticmethod def _get_response( host, metadata, query_params, session, timeout, transcoded_request, body=None): uri = transcoded_request['uri'] method = transcoded_request['method'] headers = dict(metadata) headers['Content-Type'] = 'application/json' response = getattr(session, method)( "{host}{uri}".format(host=host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params, strict=True), data=body, stream=True, ) return response def __call__(self, request: prediction_service.GenerateContentRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: Optional[float]=None, metadata: Sequence[Tuple[str, Union[str, bytes]]]=(), ) -> rest_streaming.ResponseIterator: r"""Call the stream generate content method over HTTP. Args: request (~.prediction_service.GenerateContentRequest): The request object. Request message for [PredictionService.GenerateContent]. 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: ~.prediction_service.GenerateContentResponse: Response message for [PredictionService.GenerateContent]. """ http_options = _BasePredictionServiceRestTransport._BaseStreamGenerateContent._get_http_options() request, metadata = self._interceptor.pre_stream_generate_content(request, metadata) transcoded_request = _BasePredictionServiceRestTransport._BaseStreamGenerateContent._get_transcoded_request(http_options, request) body = _BasePredictionServiceRestTransport._BaseStreamGenerateContent._get_request_body_json(transcoded_request) # Jsonify the query params query_params = _BasePredictionServiceRestTransport._BaseStreamGenerateContent._get_query_params_json(transcoded_request) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER request_url = "{host}{uri}".format(host=self._host, uri=transcoded_request['uri']) method = transcoded_request['method'] try: request_payload = type(request).to_json(request) except: request_payload = None http_request = { "payload": request_payload, "requestMethod": method, "requestUrl": request_url, "headers": dict(metadata), } _LOGGER.debug( f"Sending request for google.cloud.aiplatform_v1beta1.PredictionServiceClient.StreamGenerateContent", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "StreamGenerateContent", "httpRequest": http_request, "metadata": http_request["headers"], }, ) # Send the request response = PredictionServiceRestTransport._StreamGenerateContent._get_response(self._host, metadata, query_params, self._session, timeout, transcoded_request, body) # In case of error, raise the appropriate core_exceptions.GoogleAPICallError exception # subclass. if response.status_code >= 400: raise core_exceptions.from_http_response(response) # Return the response resp = rest_streaming.ResponseIterator(response, prediction_service.GenerateContentResponse) resp = self._interceptor.post_stream_generate_content(resp) response_metadata = [(k, str(v)) for k, v in response.headers.items()] resp, _ = self._interceptor.post_stream_generate_content_with_metadata(resp, response_metadata) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER http_response = { "headers": dict(response.headers), "status": response.status_code, } _LOGGER.debug( "Received response for google.cloud.aiplatform_v1beta1.PredictionServiceClient.stream_generate_content", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "StreamGenerateContent", "metadata": http_response["headers"], "httpResponse": http_response, }, ) return resp class _StreamingPredict(_BasePredictionServiceRestTransport._BaseStreamingPredict, PredictionServiceRestStub): def __hash__(self): return hash("PredictionServiceRestTransport.StreamingPredict") def __call__(self, request: prediction_service.StreamingPredictRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: Optional[float]=None, metadata: Sequence[Tuple[str, Union[str, bytes]]]=(), ) -> rest_streaming.ResponseIterator: raise NotImplementedError( "Method StreamingPredict is not available over REST transport" ) class _StreamingRawPredict(_BasePredictionServiceRestTransport._BaseStreamingRawPredict, PredictionServiceRestStub): def __hash__(self): return hash("PredictionServiceRestTransport.StreamingRawPredict") def __call__(self, request: prediction_service.StreamingRawPredictRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: Optional[float]=None, metadata: Sequence[Tuple[str, Union[str, bytes]]]=(), ) -> rest_streaming.ResponseIterator: raise NotImplementedError( "Method StreamingRawPredict is not available over REST transport" ) class _StreamRawPredict(_BasePredictionServiceRestTransport._BaseStreamRawPredict, PredictionServiceRestStub): def __hash__(self): return hash("PredictionServiceRestTransport.StreamRawPredict") @staticmethod def _get_response( host, metadata, query_params, session, timeout, transcoded_request, body=None): uri = transcoded_request['uri'] method = transcoded_request['method'] headers = dict(metadata) headers['Content-Type'] = 'application/json' response = getattr(session, method)( "{host}{uri}".format(host=host, uri=uri), timeout=timeout, headers=headers, params=rest_helpers.flatten_query_params(query_params, strict=True), data=body, stream=True, ) return response def __call__(self, request: prediction_service.StreamRawPredictRequest, *, retry: OptionalRetry=gapic_v1.method.DEFAULT, timeout: Optional[float]=None, metadata: Sequence[Tuple[str, Union[str, bytes]]]=(), ) -> rest_streaming.ResponseIterator: r"""Call the stream raw predict method over HTTP. Args: request (~.prediction_service.StreamRawPredictRequest): The request object. Request message for [PredictionService.StreamRawPredict][google.cloud.aiplatform.v1beta1.PredictionService.StreamRawPredict]. 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: ~.httpbody_pb2.HttpBody: Message that represents an arbitrary HTTP body. It should only be used for payload formats that can't be represented as JSON, such as raw binary or an HTML page. This message can be used both in streaming and non-streaming API methods in the request as well as the response. It can be used as a top-level request field, which is convenient if one wants to extract parameters from either the URL or HTTP template into the request fields and also want access to the raw HTTP body. Example: :: message GetResourceRequest { // A unique request id. string request_id = 1; // The raw HTTP body is bound to this field. google.api.HttpBody http_body = 2; } service ResourceService { rpc GetResource(GetResourceRequest) returns (google.api.HttpBody); rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty); } Example with streaming methods: :: service CaldavService { rpc GetCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); rpc UpdateCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); } Use of this type only changes how the request and response bodies are handled, all other features will continue to work unchanged. """ http_options = _BasePredictionServiceRestTransport._BaseStreamRawPredict._get_http_options() request, metadata = self._interceptor.pre_stream_raw_predict(request, metadata) transcoded_request = _BasePredictionServiceRestTransport._BaseStreamRawPredict._get_transcoded_request(http_options, request) body = _BasePredictionServiceRestTransport._BaseStreamRawPredict._get_request_body_json(transcoded_request) # Jsonify the query params query_params = _BasePredictionServiceRestTransport._BaseStreamRawPredict._get_query_params_json(transcoded_request) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER request_url = "{host}{uri}".format(host=self._host, uri=transcoded_request['uri']) method = transcoded_request['method'] try: request_payload = json_format.MessageToJson(request) except: request_payload = None http_request = { "payload": request_payload, "requestMethod": method, "requestUrl": request_url, "headers": dict(metadata), } _LOGGER.debug( f"Sending request for google.cloud.aiplatform_v1beta1.PredictionServiceClient.StreamRawPredict", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "StreamRawPredict", "httpRequest": http_request, "metadata": http_request["headers"], }, ) # Send the request response = PredictionServiceRestTransport._StreamRawPredict._get_response(self._host, metadata, query_params, self._session, timeout, transcoded_request, body) # In case of error, raise the appropriate core_exceptions.GoogleAPICallError exception # subclass. if response.status_code >= 400: raise core_exceptions.from_http_response(response) # Return the response resp = rest_streaming.ResponseIterator(response, httpbody_pb2.HttpBody) resp = self._interceptor.post_stream_raw_predict(resp) response_metadata = [(k, str(v)) for k, v in response.headers.items()] resp, _ = self._interceptor.post_stream_raw_predict_with_metadata(resp, response_metadata) if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor(logging.DEBUG): # pragma: NO COVER http_response = { "headers": dict(response.headers), "status": response.status_code, } _LOGGER.debug( "Received response for google.cloud.aiplatform_v1beta1.PredictionServiceClient.stream_raw_predict", extra = { "serviceName": "google.cloud.aiplatform.v1beta1.PredictionService", "rpcName": "StreamRawPredict", "metadata": http_response["headers"], "httpResponse": http_response, }, ) return resp @property def chat_completions(self) -> Callable[ [prediction_service.ChatCompletionsRequest], httpbody_pb2.HttpBody]: # The return type is fine, but mypy isn't sophisticated enough to determine what's going on here. # In C++ this would require a dynamic_cast return self._ChatCompletions(self._session, self._host, self._interceptor) # type: ignore @property def count_tokens(self) -> Callable[ [prediction_service.CountTokensRequest], prediction_service.CountTokensResponse]: # The return type is fine, but mypy isn't sophisticated enough to determine what's going on here. # In C++ this would require a dynamic_cast return self._CountTokens(self._session, self._host, self._interceptor) # type: ignore @property def direct_predict(self) -> Callable[ [prediction_service.DirectPredictRequest], prediction_service.DirectPredictResponse]: # The return type is fine, but mypy isn't sophisticated enough to determine what's going on here. # In C++ this would require a dynamic_cast return self._DirectPredict(self._session, self._host, self._interceptor) # type: ignore @property def direct_raw_predict(self) -> Callable[ [prediction_service.DirectRawPredictRequest], prediction_service.DirectRawPredictResponse]: # The return type is fine, but mypy isn't sophisticated enough to determine what's going on here. # In C++ this would require a dynamic_cast return self._DirectRawPredict(self._session, self._host, self._interceptor) # type: ignore @property def embed_content(self) -> Callable[ [prediction_service.EmbedContentRequest], prediction_service.EmbedContentResponse]: # The return type is fine, but mypy isn't sophisticated enough to determine what's going on here. # In C++ this would require a dynamic_cast return self._EmbedContent(self._session, self._host, self._interceptor) # type: ignore @property def explain(self) -> Callable[ [prediction_service.ExplainRequest], prediction_service.ExplainResponse]: # The return type is fine, but mypy isn't sophisticated enough to determine what's going on here. # In C++ this would require a dynamic_cast return self._Explain(self._session, self._host, self._interceptor) # type: ignore @property def fetch_predict_operation(self) -> Callable[ [prediction_service.FetchPredictOperationRequest], operations_pb2.Operation]: # The return type is fine, but mypy isn't sophisticated enough to determine what's going on here. # In C++ this would require a dynamic_cast return self._FetchPredictOperation(self._session, self._host, self._interceptor) # type: ignore @property def generate_content(self) -> Callable[ [prediction_service.GenerateContentRequest], prediction_service.GenerateContentResponse]: # The return type is fine, but mypy isn't sophisticated enough to determine what's going on here. # In C++ this would require a dynamic_cast return self._GenerateContent(self._session, self._host, self._interceptor) # type: ignore @property def invoke(self) -> Callable[ [prediction_service.InvokeRequest], httpbody_pb2.HttpBody]: # The return type is fine, but mypy isn't sophisticated enough to determine what's going on here. # In C++ this would require a dynamic_cast return self._Invoke(self._session, self._host, self._interceptor) # type: ignore @property def predict(self) -> Callable[ [prediction_service.PredictRequest], prediction_service.PredictResponse]: # The return type is fine, but mypy isn't sophisticated enough to determine what's going on here. # In C++ this would require a dynamic_cast return self._Predict(self._session, self._host, self._interceptor) # type: ignore @property def predict_long_running(self) -> Callable[ [prediction_service.PredictLongRunningRequest], operations_pb2.Operation]: # The return type is fine, but mypy isn't sophisticated enough to determine what's going on here. # In C++ this would require a dynamic_cast return self._PredictLongRunning(self._session, self._host, self._interceptor) # type: ignore @property def raw_predict(self) -> Callable[ [prediction_service.RawPredictRequest], httpbody_pb2.HttpBody]: # The return type is fine, but mypy isn't sophisticated enough to determine what's going on here. # In C++ this would require a dynamic_cast return self._RawPredict(self._session, self._host, self._interceptor) # type: ignore @property def server_streaming_predict(self) -> Callable[ [prediction_service.StreamingPredictRequest], prediction_service.StreamingPredictResponse]: # The return type is fine, but mypy isn't sophisticated enough to determine what's going on here. # In C++ this would require a dynamic_cast return self._ServerStreamingPredict(self._session, self._host, self._interceptor) # type: ignore @property def stream_direct_predict(self) -> Callable[ [prediction_service.StreamDirectPredictRequest], prediction_service.StreamDirectPredictResponse]: # The return type is fine, but mypy isn't sophisticated enough to determine what's going on here. # In C++ this would require a dynamic_cast return self._StreamDirectPredict(self._session, self._host, self._interceptor) # type: ignore @property def stream_direct_raw_predict(self) -> Callable[ [prediction_service.StreamDirectRawPredictRequest], prediction_service.StreamDirectRawPredictResponse]: # The return type is fine, but mypy isn't sophisticated enough to determine what's going on here. # In C++ this would require a dynamic_cast return self._StreamDirectRawPredict(self._session, self._host, self._interceptor) # type: ignore @property def stream_generate_content(self) -> Callable[ [prediction_service.GenerateContentRequest], prediction_service.GenerateContentResponse]: # The return type is fine, but mypy isn't sophisticated enough to determine what's going on here. # In C++ this would require a dynamic_cast return self._StreamGenerateContent(self._session, self._host, self._interceptor) # type: ignore @property def streaming_predict(self) -> Callable[ [prediction_service.StreamingPredictRequest], prediction_service.StreamingPredictResponse]: # The return type is fine, but mypy isn't sophisticated enough to determine what's going on here. # In C++ this would require a dynamic_cast return self._StreamingPredict(self._session, self._host, self._interceptor) # type: ignore @property def streaming_raw_predict(self) -> Callable[ [prediction_service.StreamingRawPredictRequest], prediction_service.StreamingRawPredictResponse]: # The return type is fine, but mypy isn't sophisticated enough to determine what's going on here. # In C++ this would require a dynamic_cast return self._StreamingRawPredict(self._session, self._host, self._interceptor) # type: ignore @property def stream_raw_predict(self) -> Callable[ [prediction_service.StreamRawPredictRequest], httpbody_pb2.HttpBody]: # The return type is fine, but mypy isn't sophisticated enough to determine what's going on here. # In C++ this would require a dynamic_cast return self._StreamRawPredict(self._session, self._host, self._interceptor) # type: ignore @property def kind(self) -> str: return "rest" def close(self): self._session.close() __all__=( 'PredictionServiceRestTransport', )