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Add deprecation warnings and raise exception for already deprecated ones
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airflow/providers/google/cloud/hooks/vertex_ai/prediction_service.py
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# Licensed to the Apache Software Foundation (ASF) under one | ||
# or more contributor license agreements. See the NOTICE file | ||
# distributed with this work for additional information | ||
# regarding copyright ownership. The ASF licenses this file | ||
# to you 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. | ||
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from __future__ import annotations | ||
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from typing import TYPE_CHECKING, Sequence | ||
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from google.api_core.client_options import ClientOptions | ||
from google.api_core.gapic_v1.method import DEFAULT, _MethodDefault | ||
from google.cloud.aiplatform_v1 import PredictionServiceClient | ||
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from airflow.providers.google.common.consts import CLIENT_INFO | ||
from airflow.providers.google.common.hooks.base_google import PROVIDE_PROJECT_ID, GoogleBaseHook | ||
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if TYPE_CHECKING: | ||
from google.api_core.retry import Retry | ||
from google.cloud.aiplatform_v1.types import PredictResponse | ||
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class PredictionServiceHook(GoogleBaseHook): | ||
"""Hook for Google Cloud Vertex AI Prediction API.""" | ||
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def get_prediction_service_client(self, region: str | None = None) -> PredictionServiceClient: | ||
""" | ||
Return PredictionServiceClient object. | ||
:param region: The ID of the Google Cloud region that the service belongs to. Default is None. | ||
:return: `google.cloud.aiplatform_v1.services.prediction_service.client.PredictionServiceClient` instance. | ||
""" | ||
if region and region != "global": | ||
client_options = ClientOptions(api_endpoint=f"{region}-aiplatform.googleapis.com:443") | ||
else: | ||
client_options = ClientOptions() | ||
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return PredictionServiceClient( | ||
credentials=self.get_credentials(), client_info=CLIENT_INFO, client_options=client_options | ||
) | ||
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@GoogleBaseHook.fallback_to_default_project_id | ||
def predict( | ||
self, | ||
endpoint_id: str, | ||
instances: list[str], | ||
location: str, | ||
project_id: str = PROVIDE_PROJECT_ID, | ||
parameters: dict[str, str] | None = None, | ||
retry: Retry | _MethodDefault = DEFAULT, | ||
timeout: float | None = None, | ||
metadata: Sequence[tuple[str, str]] = (), | ||
) -> PredictResponse: | ||
""" | ||
Perform an online prediction and returns the prediction result in the response. | ||
:param endpoint_id: Name of the endpoint_id requested to serve the prediction. | ||
:param instances: Required. The instances that are the input to the prediction call. A DeployedModel | ||
may have an upper limit on the number of instances it supports per request, and when it is | ||
exceeded the prediction call errors in case of AutoML Models, or, in case of customer created | ||
Models, the behaviour is as documented by that Model. | ||
:param parameters: Additional domain-specific parameters, any string must be up to 25000 characters long. | ||
:param project_id: ID of the Google Cloud project where model is located if None then | ||
default project_id is used. | ||
:param location: The location of the project. | ||
:param retry: A retry object used to retry requests. If `None` is specified, requests will not be | ||
retried. | ||
:param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if | ||
`retry` is specified, the timeout applies to each individual attempt. | ||
:param metadata: Additional metadata that is provided to the method. | ||
""" | ||
client = self.get_prediction_service_client(location) | ||
endpoint = f"projects/{project_id}/locations/{location}/endpoints/{endpoint_id}" | ||
return client.predict( | ||
request={"endpoint": endpoint, "instances": instances, "parameters": parameters}, | ||
retry=retry, | ||
timeout=timeout, | ||
metadata=metadata, | ||
) |
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