6928 lines
206 KiB
Python
6928 lines
206 KiB
Python
# Copyright 2025 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# Code generated by the Google Gen AI SDK generator DO NOT EDIT.
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import logging
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from typing import Any, AsyncIterator, Awaitable, Iterator, Optional, Union
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from urllib.parse import urlencode
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from . import _api_module
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from . import _common
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from . import _extra_utils
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from . import _transformers as t
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from . import types
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from ._api_client import BaseApiClient
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from ._common import get_value_by_path as getv
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from ._common import set_value_by_path as setv
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from .pagers import AsyncPager, Pager
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logger = logging.getLogger('google_genai.models')
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def _Part_to_mldev(
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api_client: BaseApiClient,
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from_object: Union[dict[str, Any], object],
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parent_object: Optional[dict[str, Any]] = None,
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) -> dict[str, Any]:
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to_object: dict[str, Any] = {}
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if getv(from_object, ['video_metadata']) is not None:
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raise ValueError('video_metadata parameter is not supported in Gemini API.')
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if getv(from_object, ['thought']) is not None:
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setv(to_object, ['thought'], getv(from_object, ['thought']))
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if getv(from_object, ['code_execution_result']) is not None:
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setv(
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to_object,
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['codeExecutionResult'],
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getv(from_object, ['code_execution_result']),
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)
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if getv(from_object, ['executable_code']) is not None:
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setv(to_object, ['executableCode'], getv(from_object, ['executable_code']))
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if getv(from_object, ['file_data']) is not None:
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setv(to_object, ['fileData'], getv(from_object, ['file_data']))
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if getv(from_object, ['function_call']) is not None:
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setv(to_object, ['functionCall'], getv(from_object, ['function_call']))
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if getv(from_object, ['function_response']) is not None:
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setv(
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to_object,
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['functionResponse'],
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getv(from_object, ['function_response']),
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)
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if getv(from_object, ['inline_data']) is not None:
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setv(to_object, ['inlineData'], getv(from_object, ['inline_data']))
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if getv(from_object, ['text']) is not None:
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setv(to_object, ['text'], getv(from_object, ['text']))
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return to_object
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def _Content_to_mldev(
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api_client: BaseApiClient,
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from_object: Union[dict[str, Any], object],
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parent_object: Optional[dict[str, Any]] = None,
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) -> dict[str, Any]:
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to_object: dict[str, Any] = {}
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if getv(from_object, ['parts']) is not None:
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setv(
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to_object,
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['parts'],
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[
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_Part_to_mldev(api_client, item, to_object)
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for item in getv(from_object, ['parts'])
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],
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)
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if getv(from_object, ['role']) is not None:
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setv(to_object, ['role'], getv(from_object, ['role']))
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return to_object
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def _Schema_to_mldev(
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api_client: BaseApiClient,
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from_object: Union[dict[str, Any], object],
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parent_object: Optional[dict[str, Any]] = None,
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) -> dict[str, Any]:
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to_object: dict[str, Any] = {}
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if getv(from_object, ['example']) is not None:
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raise ValueError('example parameter is not supported in Gemini API.')
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if getv(from_object, ['pattern']) is not None:
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raise ValueError('pattern parameter is not supported in Gemini API.')
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if getv(from_object, ['default']) is not None:
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raise ValueError('default parameter is not supported in Gemini API.')
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if getv(from_object, ['max_length']) is not None:
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raise ValueError('max_length parameter is not supported in Gemini API.')
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if getv(from_object, ['min_length']) is not None:
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raise ValueError('min_length parameter is not supported in Gemini API.')
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if getv(from_object, ['min_properties']) is not None:
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raise ValueError('min_properties parameter is not supported in Gemini API.')
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if getv(from_object, ['max_properties']) is not None:
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raise ValueError('max_properties parameter is not supported in Gemini API.')
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if getv(from_object, ['any_of']) is not None:
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setv(to_object, ['anyOf'], getv(from_object, ['any_of']))
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if getv(from_object, ['description']) is not None:
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setv(to_object, ['description'], getv(from_object, ['description']))
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if getv(from_object, ['enum']) is not None:
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setv(to_object, ['enum'], getv(from_object, ['enum']))
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if getv(from_object, ['format']) is not None:
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setv(to_object, ['format'], getv(from_object, ['format']))
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if getv(from_object, ['items']) is not None:
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setv(to_object, ['items'], getv(from_object, ['items']))
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if getv(from_object, ['max_items']) is not None:
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setv(to_object, ['maxItems'], getv(from_object, ['max_items']))
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if getv(from_object, ['maximum']) is not None:
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setv(to_object, ['maximum'], getv(from_object, ['maximum']))
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if getv(from_object, ['min_items']) is not None:
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setv(to_object, ['minItems'], getv(from_object, ['min_items']))
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if getv(from_object, ['minimum']) is not None:
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setv(to_object, ['minimum'], getv(from_object, ['minimum']))
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if getv(from_object, ['nullable']) is not None:
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setv(to_object, ['nullable'], getv(from_object, ['nullable']))
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if getv(from_object, ['properties']) is not None:
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setv(to_object, ['properties'], getv(from_object, ['properties']))
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if getv(from_object, ['property_ordering']) is not None:
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setv(
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to_object,
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['propertyOrdering'],
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getv(from_object, ['property_ordering']),
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)
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if getv(from_object, ['required']) is not None:
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setv(to_object, ['required'], getv(from_object, ['required']))
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if getv(from_object, ['title']) is not None:
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setv(to_object, ['title'], getv(from_object, ['title']))
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if getv(from_object, ['type']) is not None:
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setv(to_object, ['type'], getv(from_object, ['type']))
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return to_object
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def _ModelSelectionConfig_to_mldev(
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api_client: BaseApiClient,
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from_object: Union[dict[str, Any], object],
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parent_object: Optional[dict[str, Any]] = None,
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) -> dict[str, Any]:
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to_object: dict[str, Any] = {}
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if getv(from_object, ['feature_selection_preference']) is not None:
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raise ValueError(
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'feature_selection_preference parameter is not supported in Gemini API.'
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)
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return to_object
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def _SafetySetting_to_mldev(
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api_client: BaseApiClient,
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from_object: Union[dict[str, Any], object],
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parent_object: Optional[dict[str, Any]] = None,
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) -> dict[str, Any]:
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to_object: dict[str, Any] = {}
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if getv(from_object, ['method']) is not None:
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raise ValueError('method parameter is not supported in Gemini API.')
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if getv(from_object, ['category']) is not None:
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setv(to_object, ['category'], getv(from_object, ['category']))
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if getv(from_object, ['threshold']) is not None:
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setv(to_object, ['threshold'], getv(from_object, ['threshold']))
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return to_object
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def _FunctionDeclaration_to_mldev(
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api_client: BaseApiClient,
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from_object: Union[dict[str, Any], object],
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parent_object: Optional[dict[str, Any]] = None,
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) -> dict[str, Any]:
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to_object: dict[str, Any] = {}
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if getv(from_object, ['response']) is not None:
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raise ValueError('response parameter is not supported in Gemini API.')
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if getv(from_object, ['description']) is not None:
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setv(to_object, ['description'], getv(from_object, ['description']))
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if getv(from_object, ['name']) is not None:
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setv(to_object, ['name'], getv(from_object, ['name']))
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if getv(from_object, ['parameters']) is not None:
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setv(to_object, ['parameters'], getv(from_object, ['parameters']))
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return to_object
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def _GoogleSearch_to_mldev(
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api_client: BaseApiClient,
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from_object: Union[dict[str, Any], object],
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parent_object: Optional[dict[str, Any]] = None,
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) -> dict[str, Any]:
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to_object: dict[str, Any] = {}
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return to_object
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def _DynamicRetrievalConfig_to_mldev(
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api_client: BaseApiClient,
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from_object: Union[dict[str, Any], object],
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parent_object: Optional[dict[str, Any]] = None,
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) -> dict[str, Any]:
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to_object: dict[str, Any] = {}
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if getv(from_object, ['mode']) is not None:
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setv(to_object, ['mode'], getv(from_object, ['mode']))
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if getv(from_object, ['dynamic_threshold']) is not None:
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setv(
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to_object,
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['dynamicThreshold'],
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getv(from_object, ['dynamic_threshold']),
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)
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return to_object
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def _GoogleSearchRetrieval_to_mldev(
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api_client: BaseApiClient,
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from_object: Union[dict[str, Any], object],
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parent_object: Optional[dict[str, Any]] = None,
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) -> dict[str, Any]:
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to_object: dict[str, Any] = {}
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if getv(from_object, ['dynamic_retrieval_config']) is not None:
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setv(
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to_object,
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['dynamicRetrievalConfig'],
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_DynamicRetrievalConfig_to_mldev(
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api_client,
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getv(from_object, ['dynamic_retrieval_config']),
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to_object,
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),
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)
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return to_object
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def _Tool_to_mldev(
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api_client: BaseApiClient,
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from_object: Union[dict[str, Any], object],
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parent_object: Optional[dict[str, Any]] = None,
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) -> dict[str, Any]:
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to_object: dict[str, Any] = {}
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if getv(from_object, ['function_declarations']) is not None:
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setv(
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to_object,
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['functionDeclarations'],
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[
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_FunctionDeclaration_to_mldev(api_client, item, to_object)
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for item in getv(from_object, ['function_declarations'])
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],
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)
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if getv(from_object, ['retrieval']) is not None:
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raise ValueError('retrieval parameter is not supported in Gemini API.')
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if getv(from_object, ['google_search']) is not None:
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setv(
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to_object,
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['googleSearch'],
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_GoogleSearch_to_mldev(
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api_client, getv(from_object, ['google_search']), to_object
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),
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)
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if getv(from_object, ['google_search_retrieval']) is not None:
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setv(
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to_object,
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['googleSearchRetrieval'],
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_GoogleSearchRetrieval_to_mldev(
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api_client,
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getv(from_object, ['google_search_retrieval']),
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to_object,
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),
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)
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if getv(from_object, ['code_execution']) is not None:
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setv(to_object, ['codeExecution'], getv(from_object, ['code_execution']))
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return to_object
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def _FunctionCallingConfig_to_mldev(
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api_client: BaseApiClient,
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from_object: Union[dict[str, Any], object],
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parent_object: Optional[dict[str, Any]] = None,
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) -> dict[str, Any]:
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to_object: dict[str, Any] = {}
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if getv(from_object, ['mode']) is not None:
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setv(to_object, ['mode'], getv(from_object, ['mode']))
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if getv(from_object, ['allowed_function_names']) is not None:
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setv(
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to_object,
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['allowedFunctionNames'],
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getv(from_object, ['allowed_function_names']),
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)
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return to_object
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def _ToolConfig_to_mldev(
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api_client: BaseApiClient,
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from_object: Union[dict[str, Any], object],
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parent_object: Optional[dict[str, Any]] = None,
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) -> dict[str, Any]:
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to_object: dict[str, Any] = {}
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if getv(from_object, ['function_calling_config']) is not None:
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setv(
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to_object,
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['functionCallingConfig'],
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_FunctionCallingConfig_to_mldev(
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api_client,
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getv(from_object, ['function_calling_config']),
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to_object,
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),
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)
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return to_object
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def _PrebuiltVoiceConfig_to_mldev(
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api_client: BaseApiClient,
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from_object: Union[dict[str, Any], object],
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parent_object: Optional[dict[str, Any]] = None,
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) -> dict[str, Any]:
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to_object: dict[str, Any] = {}
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if getv(from_object, ['voice_name']) is not None:
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setv(to_object, ['voiceName'], getv(from_object, ['voice_name']))
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return to_object
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def _VoiceConfig_to_mldev(
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api_client: BaseApiClient,
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from_object: Union[dict[str, Any], object],
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parent_object: Optional[dict[str, Any]] = None,
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) -> dict[str, Any]:
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to_object: dict[str, Any] = {}
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if getv(from_object, ['prebuilt_voice_config']) is not None:
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setv(
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to_object,
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['prebuiltVoiceConfig'],
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_PrebuiltVoiceConfig_to_mldev(
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api_client, getv(from_object, ['prebuilt_voice_config']), to_object
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),
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)
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return to_object
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def _SpeechConfig_to_mldev(
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api_client: BaseApiClient,
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from_object: Union[dict[str, Any], object],
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parent_object: Optional[dict[str, Any]] = None,
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) -> dict[str, Any]:
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to_object: dict[str, Any] = {}
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if getv(from_object, ['voice_config']) is not None:
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setv(
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to_object,
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['voiceConfig'],
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_VoiceConfig_to_mldev(
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api_client, getv(from_object, ['voice_config']), to_object
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),
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)
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if getv(from_object, ['language_code']) is not None:
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setv(to_object, ['languageCode'], getv(from_object, ['language_code']))
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return to_object
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def _ThinkingConfig_to_mldev(
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api_client: BaseApiClient,
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from_object: Union[dict[str, Any], object],
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parent_object: Optional[dict[str, Any]] = None,
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) -> dict[str, Any]:
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to_object: dict[str, Any] = {}
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if getv(from_object, ['include_thoughts']) is not None:
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setv(
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to_object, ['includeThoughts'], getv(from_object, ['include_thoughts'])
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)
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if getv(from_object, ['thinking_budget']) is not None:
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setv(to_object, ['thinkingBudget'], getv(from_object, ['thinking_budget']))
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return to_object
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|
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def _GenerateContentConfig_to_mldev(
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api_client: BaseApiClient,
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from_object: Union[dict[str, Any], object],
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parent_object: Optional[dict[str, Any]] = None,
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) -> dict[str, Any]:
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to_object: dict[str, Any] = {}
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|
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if getv(from_object, ['system_instruction']) is not None:
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setv(
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parent_object,
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['systemInstruction'],
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_Content_to_mldev(
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api_client,
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t.t_content(api_client, getv(from_object, ['system_instruction'])),
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to_object,
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),
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)
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if getv(from_object, ['temperature']) is not None:
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setv(to_object, ['temperature'], getv(from_object, ['temperature']))
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if getv(from_object, ['top_p']) is not None:
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setv(to_object, ['topP'], getv(from_object, ['top_p']))
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|
if getv(from_object, ['top_k']) is not None:
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setv(to_object, ['topK'], getv(from_object, ['top_k']))
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if getv(from_object, ['candidate_count']) is not None:
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setv(to_object, ['candidateCount'], getv(from_object, ['candidate_count']))
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|
if getv(from_object, ['max_output_tokens']) is not None:
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setv(
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to_object, ['maxOutputTokens'], getv(from_object, ['max_output_tokens'])
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)
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if getv(from_object, ['stop_sequences']) is not None:
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setv(to_object, ['stopSequences'], getv(from_object, ['stop_sequences']))
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|
if getv(from_object, ['response_logprobs']) is not None:
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setv(
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to_object,
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['responseLogprobs'],
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getv(from_object, ['response_logprobs']),
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)
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if getv(from_object, ['logprobs']) is not None:
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setv(to_object, ['logprobs'], getv(from_object, ['logprobs']))
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|
if getv(from_object, ['presence_penalty']) is not None:
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setv(
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to_object, ['presencePenalty'], getv(from_object, ['presence_penalty'])
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)
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|
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if getv(from_object, ['frequency_penalty']) is not None:
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setv(
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to_object,
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['frequencyPenalty'],
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getv(from_object, ['frequency_penalty']),
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)
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if getv(from_object, ['seed']) is not None:
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|
setv(to_object, ['seed'], getv(from_object, ['seed']))
|
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|
|
if getv(from_object, ['response_mime_type']) is not None:
|
|
setv(
|
|
to_object,
|
|
['responseMimeType'],
|
|
getv(from_object, ['response_mime_type']),
|
|
)
|
|
|
|
if getv(from_object, ['response_schema']) is not None:
|
|
setv(
|
|
to_object,
|
|
['responseSchema'],
|
|
_Schema_to_mldev(
|
|
api_client,
|
|
t.t_schema(api_client, getv(from_object, ['response_schema'])),
|
|
to_object,
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['routing_config']) is not None:
|
|
raise ValueError('routing_config parameter is not supported in Gemini API.')
|
|
|
|
if getv(from_object, ['model_selection_config']) is not None:
|
|
raise ValueError(
|
|
'model_selection_config parameter is not supported in Gemini API.'
|
|
)
|
|
|
|
if getv(from_object, ['safety_settings']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['safetySettings'],
|
|
[
|
|
_SafetySetting_to_mldev(api_client, item, to_object)
|
|
for item in getv(from_object, ['safety_settings'])
|
|
],
|
|
)
|
|
|
|
if getv(from_object, ['tools']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['tools'],
|
|
[
|
|
_Tool_to_mldev(api_client, t.t_tool(api_client, item), to_object)
|
|
for item in t.t_tools(api_client, getv(from_object, ['tools']))
|
|
],
|
|
)
|
|
|
|
if getv(from_object, ['tool_config']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['toolConfig'],
|
|
_ToolConfig_to_mldev(
|
|
api_client, getv(from_object, ['tool_config']), to_object
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['labels']) is not None:
|
|
raise ValueError('labels parameter is not supported in Gemini API.')
|
|
|
|
if getv(from_object, ['cached_content']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['cachedContent'],
|
|
t.t_cached_content_name(
|
|
api_client, getv(from_object, ['cached_content'])
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['response_modalities']) is not None:
|
|
setv(
|
|
to_object,
|
|
['responseModalities'],
|
|
getv(from_object, ['response_modalities']),
|
|
)
|
|
|
|
if getv(from_object, ['media_resolution']) is not None:
|
|
setv(
|
|
to_object, ['mediaResolution'], getv(from_object, ['media_resolution'])
|
|
)
|
|
|
|
if getv(from_object, ['speech_config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['speechConfig'],
|
|
_SpeechConfig_to_mldev(
|
|
api_client,
|
|
t.t_speech_config(api_client, getv(from_object, ['speech_config'])),
|
|
to_object,
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['audio_timestamp']) is not None:
|
|
raise ValueError(
|
|
'audio_timestamp parameter is not supported in Gemini API.'
|
|
)
|
|
|
|
if getv(from_object, ['thinking_config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['thinkingConfig'],
|
|
_ThinkingConfig_to_mldev(
|
|
api_client, getv(from_object, ['thinking_config']), to_object
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _GenerateContentParameters_to_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['model']) is not None:
|
|
setv(
|
|
to_object,
|
|
['_url', 'model'],
|
|
t.t_model(api_client, getv(from_object, ['model'])),
|
|
)
|
|
|
|
if getv(from_object, ['contents']) is not None:
|
|
setv(
|
|
to_object,
|
|
['contents'],
|
|
[
|
|
_Content_to_mldev(api_client, item, to_object)
|
|
for item in t.t_contents(
|
|
api_client, getv(from_object, ['contents'])
|
|
)
|
|
],
|
|
)
|
|
|
|
if getv(from_object, ['config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['generationConfig'],
|
|
_GenerateContentConfig_to_mldev(
|
|
api_client, getv(from_object, ['config']), to_object
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _EmbedContentConfig_to_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
|
|
if getv(from_object, ['task_type']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['requests[]', 'taskType'],
|
|
getv(from_object, ['task_type']),
|
|
)
|
|
|
|
if getv(from_object, ['title']) is not None:
|
|
setv(parent_object, ['requests[]', 'title'], getv(from_object, ['title']))
|
|
|
|
if getv(from_object, ['output_dimensionality']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['requests[]', 'outputDimensionality'],
|
|
getv(from_object, ['output_dimensionality']),
|
|
)
|
|
|
|
if getv(from_object, ['mime_type']) is not None:
|
|
raise ValueError('mime_type parameter is not supported in Gemini API.')
|
|
|
|
if getv(from_object, ['auto_truncate']) is not None:
|
|
raise ValueError('auto_truncate parameter is not supported in Gemini API.')
|
|
|
|
return to_object
|
|
|
|
|
|
def _EmbedContentParameters_to_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['model']) is not None:
|
|
setv(
|
|
to_object,
|
|
['_url', 'model'],
|
|
t.t_model(api_client, getv(from_object, ['model'])),
|
|
)
|
|
|
|
if getv(from_object, ['contents']) is not None:
|
|
setv(
|
|
to_object,
|
|
['requests[]', 'content'],
|
|
t.t_contents_for_embed(api_client, getv(from_object, ['contents'])),
|
|
)
|
|
|
|
if getv(from_object, ['config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['config'],
|
|
_EmbedContentConfig_to_mldev(
|
|
api_client, getv(from_object, ['config']), to_object
|
|
),
|
|
)
|
|
|
|
setv(
|
|
to_object,
|
|
['requests[]', 'model'],
|
|
t.t_model(api_client, getv(from_object, ['model'])),
|
|
)
|
|
return to_object
|
|
|
|
|
|
def _GenerateImagesConfig_to_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
|
|
if getv(from_object, ['output_gcs_uri']) is not None:
|
|
raise ValueError('output_gcs_uri parameter is not supported in Gemini API.')
|
|
|
|
if getv(from_object, ['negative_prompt']) is not None:
|
|
raise ValueError(
|
|
'negative_prompt parameter is not supported in Gemini API.'
|
|
)
|
|
|
|
if getv(from_object, ['number_of_images']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'sampleCount'],
|
|
getv(from_object, ['number_of_images']),
|
|
)
|
|
|
|
if getv(from_object, ['aspect_ratio']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'aspectRatio'],
|
|
getv(from_object, ['aspect_ratio']),
|
|
)
|
|
|
|
if getv(from_object, ['guidance_scale']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'guidanceScale'],
|
|
getv(from_object, ['guidance_scale']),
|
|
)
|
|
|
|
if getv(from_object, ['seed']) is not None:
|
|
raise ValueError('seed parameter is not supported in Gemini API.')
|
|
|
|
if getv(from_object, ['safety_filter_level']) is not None:
|
|
_SafetyFilterLevel_to_mldev_enum_validate(
|
|
getv(from_object, ['safety_filter_level'])
|
|
)
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'safetySetting'],
|
|
getv(from_object, ['safety_filter_level']),
|
|
)
|
|
|
|
if getv(from_object, ['person_generation']) is not None:
|
|
_PersonGeneration_to_mldev_enum_validate(
|
|
getv(from_object, ['person_generation'])
|
|
)
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'personGeneration'],
|
|
getv(from_object, ['person_generation']),
|
|
)
|
|
|
|
if getv(from_object, ['include_safety_attributes']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'includeSafetyAttributes'],
|
|
getv(from_object, ['include_safety_attributes']),
|
|
)
|
|
|
|
if getv(from_object, ['include_rai_reason']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'includeRaiReason'],
|
|
getv(from_object, ['include_rai_reason']),
|
|
)
|
|
|
|
if getv(from_object, ['language']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'language'],
|
|
getv(from_object, ['language']),
|
|
)
|
|
|
|
if getv(from_object, ['output_mime_type']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'outputOptions', 'mimeType'],
|
|
getv(from_object, ['output_mime_type']),
|
|
)
|
|
|
|
if getv(from_object, ['output_compression_quality']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'outputOptions', 'compressionQuality'],
|
|
getv(from_object, ['output_compression_quality']),
|
|
)
|
|
|
|
if getv(from_object, ['add_watermark']) is not None:
|
|
raise ValueError('add_watermark parameter is not supported in Gemini API.')
|
|
|
|
if getv(from_object, ['enhance_prompt']) is not None:
|
|
raise ValueError('enhance_prompt parameter is not supported in Gemini API.')
|
|
|
|
return to_object
|
|
|
|
|
|
def _GenerateImagesParameters_to_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['model']) is not None:
|
|
setv(
|
|
to_object,
|
|
['_url', 'model'],
|
|
t.t_model(api_client, getv(from_object, ['model'])),
|
|
)
|
|
|
|
if getv(from_object, ['prompt']) is not None:
|
|
setv(to_object, ['instances[0]', 'prompt'], getv(from_object, ['prompt']))
|
|
|
|
if getv(from_object, ['config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['config'],
|
|
_GenerateImagesConfig_to_mldev(
|
|
api_client, getv(from_object, ['config']), to_object
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _GetModelParameters_to_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['model']) is not None:
|
|
setv(
|
|
to_object,
|
|
['_url', 'name'],
|
|
t.t_model(api_client, getv(from_object, ['model'])),
|
|
)
|
|
|
|
if getv(from_object, ['config']) is not None:
|
|
setv(to_object, ['config'], getv(from_object, ['config']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _ListModelsConfig_to_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
|
|
if getv(from_object, ['page_size']) is not None:
|
|
setv(
|
|
parent_object, ['_query', 'pageSize'], getv(from_object, ['page_size'])
|
|
)
|
|
|
|
if getv(from_object, ['page_token']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['_query', 'pageToken'],
|
|
getv(from_object, ['page_token']),
|
|
)
|
|
|
|
if getv(from_object, ['filter']) is not None:
|
|
setv(parent_object, ['_query', 'filter'], getv(from_object, ['filter']))
|
|
|
|
if getv(from_object, ['query_base']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['_url', 'models_url'],
|
|
t.t_models_url(api_client, getv(from_object, ['query_base'])),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _ListModelsParameters_to_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['config'],
|
|
_ListModelsConfig_to_mldev(
|
|
api_client, getv(from_object, ['config']), to_object
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _UpdateModelConfig_to_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
|
|
if getv(from_object, ['display_name']) is not None:
|
|
setv(parent_object, ['displayName'], getv(from_object, ['display_name']))
|
|
|
|
if getv(from_object, ['description']) is not None:
|
|
setv(parent_object, ['description'], getv(from_object, ['description']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _UpdateModelParameters_to_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['model']) is not None:
|
|
setv(
|
|
to_object,
|
|
['_url', 'name'],
|
|
t.t_model(api_client, getv(from_object, ['model'])),
|
|
)
|
|
|
|
if getv(from_object, ['config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['config'],
|
|
_UpdateModelConfig_to_mldev(
|
|
api_client, getv(from_object, ['config']), to_object
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _DeleteModelParameters_to_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['model']) is not None:
|
|
setv(
|
|
to_object,
|
|
['_url', 'name'],
|
|
t.t_model(api_client, getv(from_object, ['model'])),
|
|
)
|
|
|
|
if getv(from_object, ['config']) is not None:
|
|
setv(to_object, ['config'], getv(from_object, ['config']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _CountTokensConfig_to_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
|
|
if getv(from_object, ['system_instruction']) is not None:
|
|
raise ValueError(
|
|
'system_instruction parameter is not supported in Gemini API.'
|
|
)
|
|
|
|
if getv(from_object, ['tools']) is not None:
|
|
raise ValueError('tools parameter is not supported in Gemini API.')
|
|
|
|
if getv(from_object, ['generation_config']) is not None:
|
|
raise ValueError(
|
|
'generation_config parameter is not supported in Gemini API.'
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _CountTokensParameters_to_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['model']) is not None:
|
|
setv(
|
|
to_object,
|
|
['_url', 'model'],
|
|
t.t_model(api_client, getv(from_object, ['model'])),
|
|
)
|
|
|
|
if getv(from_object, ['contents']) is not None:
|
|
setv(
|
|
to_object,
|
|
['contents'],
|
|
[
|
|
_Content_to_mldev(api_client, item, to_object)
|
|
for item in t.t_contents(
|
|
api_client, getv(from_object, ['contents'])
|
|
)
|
|
],
|
|
)
|
|
|
|
if getv(from_object, ['config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['config'],
|
|
_CountTokensConfig_to_mldev(
|
|
api_client, getv(from_object, ['config']), to_object
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _Image_to_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['gcs_uri']) is not None:
|
|
raise ValueError('gcs_uri parameter is not supported in Gemini API.')
|
|
|
|
if getv(from_object, ['image_bytes']) is not None:
|
|
setv(
|
|
to_object,
|
|
['bytesBase64Encoded'],
|
|
t.t_bytes(api_client, getv(from_object, ['image_bytes'])),
|
|
)
|
|
|
|
if getv(from_object, ['mime_type']) is not None:
|
|
setv(to_object, ['mimeType'], getv(from_object, ['mime_type']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _GenerateVideosConfig_to_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
|
|
if getv(from_object, ['number_of_videos']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'sampleCount'],
|
|
getv(from_object, ['number_of_videos']),
|
|
)
|
|
|
|
if getv(from_object, ['output_gcs_uri']) is not None:
|
|
raise ValueError('output_gcs_uri parameter is not supported in Gemini API.')
|
|
|
|
if getv(from_object, ['fps']) is not None:
|
|
raise ValueError('fps parameter is not supported in Gemini API.')
|
|
|
|
if getv(from_object, ['duration_seconds']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'durationSeconds'],
|
|
getv(from_object, ['duration_seconds']),
|
|
)
|
|
|
|
if getv(from_object, ['seed']) is not None:
|
|
raise ValueError('seed parameter is not supported in Gemini API.')
|
|
|
|
if getv(from_object, ['aspect_ratio']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'aspectRatio'],
|
|
getv(from_object, ['aspect_ratio']),
|
|
)
|
|
|
|
if getv(from_object, ['resolution']) is not None:
|
|
raise ValueError('resolution parameter is not supported in Gemini API.')
|
|
|
|
if getv(from_object, ['person_generation']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'personGeneration'],
|
|
getv(from_object, ['person_generation']),
|
|
)
|
|
|
|
if getv(from_object, ['pubsub_topic']) is not None:
|
|
raise ValueError('pubsub_topic parameter is not supported in Gemini API.')
|
|
|
|
if getv(from_object, ['negative_prompt']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'negativePrompt'],
|
|
getv(from_object, ['negative_prompt']),
|
|
)
|
|
|
|
if getv(from_object, ['enhance_prompt']) is not None:
|
|
raise ValueError('enhance_prompt parameter is not supported in Gemini API.')
|
|
|
|
return to_object
|
|
|
|
|
|
def _GenerateVideosParameters_to_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['model']) is not None:
|
|
setv(
|
|
to_object,
|
|
['_url', 'model'],
|
|
t.t_model(api_client, getv(from_object, ['model'])),
|
|
)
|
|
|
|
if getv(from_object, ['prompt']) is not None:
|
|
setv(to_object, ['instances[0]', 'prompt'], getv(from_object, ['prompt']))
|
|
|
|
if getv(from_object, ['image']) is not None:
|
|
setv(
|
|
to_object,
|
|
['instances[0]', 'image'],
|
|
_Image_to_mldev(api_client, getv(from_object, ['image']), to_object),
|
|
)
|
|
|
|
if getv(from_object, ['config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['config'],
|
|
_GenerateVideosConfig_to_mldev(
|
|
api_client, getv(from_object, ['config']), to_object
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _Part_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['video_metadata']) is not None:
|
|
setv(to_object, ['videoMetadata'], getv(from_object, ['video_metadata']))
|
|
|
|
if getv(from_object, ['thought']) is not None:
|
|
setv(to_object, ['thought'], getv(from_object, ['thought']))
|
|
|
|
if getv(from_object, ['code_execution_result']) is not None:
|
|
setv(
|
|
to_object,
|
|
['codeExecutionResult'],
|
|
getv(from_object, ['code_execution_result']),
|
|
)
|
|
|
|
if getv(from_object, ['executable_code']) is not None:
|
|
setv(to_object, ['executableCode'], getv(from_object, ['executable_code']))
|
|
|
|
if getv(from_object, ['file_data']) is not None:
|
|
setv(to_object, ['fileData'], getv(from_object, ['file_data']))
|
|
|
|
if getv(from_object, ['function_call']) is not None:
|
|
setv(to_object, ['functionCall'], getv(from_object, ['function_call']))
|
|
|
|
if getv(from_object, ['function_response']) is not None:
|
|
setv(
|
|
to_object,
|
|
['functionResponse'],
|
|
getv(from_object, ['function_response']),
|
|
)
|
|
|
|
if getv(from_object, ['inline_data']) is not None:
|
|
setv(to_object, ['inlineData'], getv(from_object, ['inline_data']))
|
|
|
|
if getv(from_object, ['text']) is not None:
|
|
setv(to_object, ['text'], getv(from_object, ['text']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _Content_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['parts']) is not None:
|
|
setv(
|
|
to_object,
|
|
['parts'],
|
|
[
|
|
_Part_to_vertex(api_client, item, to_object)
|
|
for item in getv(from_object, ['parts'])
|
|
],
|
|
)
|
|
|
|
if getv(from_object, ['role']) is not None:
|
|
setv(to_object, ['role'], getv(from_object, ['role']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _Schema_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['example']) is not None:
|
|
setv(to_object, ['example'], getv(from_object, ['example']))
|
|
|
|
if getv(from_object, ['pattern']) is not None:
|
|
setv(to_object, ['pattern'], getv(from_object, ['pattern']))
|
|
|
|
if getv(from_object, ['default']) is not None:
|
|
setv(to_object, ['default'], getv(from_object, ['default']))
|
|
|
|
if getv(from_object, ['max_length']) is not None:
|
|
setv(to_object, ['maxLength'], getv(from_object, ['max_length']))
|
|
|
|
if getv(from_object, ['min_length']) is not None:
|
|
setv(to_object, ['minLength'], getv(from_object, ['min_length']))
|
|
|
|
if getv(from_object, ['min_properties']) is not None:
|
|
setv(to_object, ['minProperties'], getv(from_object, ['min_properties']))
|
|
|
|
if getv(from_object, ['max_properties']) is not None:
|
|
setv(to_object, ['maxProperties'], getv(from_object, ['max_properties']))
|
|
|
|
if getv(from_object, ['any_of']) is not None:
|
|
setv(to_object, ['anyOf'], getv(from_object, ['any_of']))
|
|
|
|
if getv(from_object, ['description']) is not None:
|
|
setv(to_object, ['description'], getv(from_object, ['description']))
|
|
|
|
if getv(from_object, ['enum']) is not None:
|
|
setv(to_object, ['enum'], getv(from_object, ['enum']))
|
|
|
|
if getv(from_object, ['format']) is not None:
|
|
setv(to_object, ['format'], getv(from_object, ['format']))
|
|
|
|
if getv(from_object, ['items']) is not None:
|
|
setv(to_object, ['items'], getv(from_object, ['items']))
|
|
|
|
if getv(from_object, ['max_items']) is not None:
|
|
setv(to_object, ['maxItems'], getv(from_object, ['max_items']))
|
|
|
|
if getv(from_object, ['maximum']) is not None:
|
|
setv(to_object, ['maximum'], getv(from_object, ['maximum']))
|
|
|
|
if getv(from_object, ['min_items']) is not None:
|
|
setv(to_object, ['minItems'], getv(from_object, ['min_items']))
|
|
|
|
if getv(from_object, ['minimum']) is not None:
|
|
setv(to_object, ['minimum'], getv(from_object, ['minimum']))
|
|
|
|
if getv(from_object, ['nullable']) is not None:
|
|
setv(to_object, ['nullable'], getv(from_object, ['nullable']))
|
|
|
|
if getv(from_object, ['properties']) is not None:
|
|
setv(to_object, ['properties'], getv(from_object, ['properties']))
|
|
|
|
if getv(from_object, ['property_ordering']) is not None:
|
|
setv(
|
|
to_object,
|
|
['propertyOrdering'],
|
|
getv(from_object, ['property_ordering']),
|
|
)
|
|
|
|
if getv(from_object, ['required']) is not None:
|
|
setv(to_object, ['required'], getv(from_object, ['required']))
|
|
|
|
if getv(from_object, ['title']) is not None:
|
|
setv(to_object, ['title'], getv(from_object, ['title']))
|
|
|
|
if getv(from_object, ['type']) is not None:
|
|
setv(to_object, ['type'], getv(from_object, ['type']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _ModelSelectionConfig_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['feature_selection_preference']) is not None:
|
|
setv(
|
|
to_object,
|
|
['featureSelectionPreference'],
|
|
getv(from_object, ['feature_selection_preference']),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _SafetySetting_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['method']) is not None:
|
|
setv(to_object, ['method'], getv(from_object, ['method']))
|
|
|
|
if getv(from_object, ['category']) is not None:
|
|
setv(to_object, ['category'], getv(from_object, ['category']))
|
|
|
|
if getv(from_object, ['threshold']) is not None:
|
|
setv(to_object, ['threshold'], getv(from_object, ['threshold']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _FunctionDeclaration_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['response']) is not None:
|
|
setv(
|
|
to_object,
|
|
['response'],
|
|
_Schema_to_vertex(
|
|
api_client, getv(from_object, ['response']), to_object
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['description']) is not None:
|
|
setv(to_object, ['description'], getv(from_object, ['description']))
|
|
|
|
if getv(from_object, ['name']) is not None:
|
|
setv(to_object, ['name'], getv(from_object, ['name']))
|
|
|
|
if getv(from_object, ['parameters']) is not None:
|
|
setv(to_object, ['parameters'], getv(from_object, ['parameters']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _GoogleSearch_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
|
|
return to_object
|
|
|
|
|
|
def _DynamicRetrievalConfig_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['mode']) is not None:
|
|
setv(to_object, ['mode'], getv(from_object, ['mode']))
|
|
|
|
if getv(from_object, ['dynamic_threshold']) is not None:
|
|
setv(
|
|
to_object,
|
|
['dynamicThreshold'],
|
|
getv(from_object, ['dynamic_threshold']),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _GoogleSearchRetrieval_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['dynamic_retrieval_config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['dynamicRetrievalConfig'],
|
|
_DynamicRetrievalConfig_to_vertex(
|
|
api_client,
|
|
getv(from_object, ['dynamic_retrieval_config']),
|
|
to_object,
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _Tool_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['function_declarations']) is not None:
|
|
setv(
|
|
to_object,
|
|
['functionDeclarations'],
|
|
[
|
|
_FunctionDeclaration_to_vertex(api_client, item, to_object)
|
|
for item in getv(from_object, ['function_declarations'])
|
|
],
|
|
)
|
|
|
|
if getv(from_object, ['retrieval']) is not None:
|
|
setv(to_object, ['retrieval'], getv(from_object, ['retrieval']))
|
|
|
|
if getv(from_object, ['google_search']) is not None:
|
|
setv(
|
|
to_object,
|
|
['googleSearch'],
|
|
_GoogleSearch_to_vertex(
|
|
api_client, getv(from_object, ['google_search']), to_object
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['google_search_retrieval']) is not None:
|
|
setv(
|
|
to_object,
|
|
['googleSearchRetrieval'],
|
|
_GoogleSearchRetrieval_to_vertex(
|
|
api_client,
|
|
getv(from_object, ['google_search_retrieval']),
|
|
to_object,
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['code_execution']) is not None:
|
|
setv(to_object, ['codeExecution'], getv(from_object, ['code_execution']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _FunctionCallingConfig_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['mode']) is not None:
|
|
setv(to_object, ['mode'], getv(from_object, ['mode']))
|
|
|
|
if getv(from_object, ['allowed_function_names']) is not None:
|
|
setv(
|
|
to_object,
|
|
['allowedFunctionNames'],
|
|
getv(from_object, ['allowed_function_names']),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _ToolConfig_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['function_calling_config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['functionCallingConfig'],
|
|
_FunctionCallingConfig_to_vertex(
|
|
api_client,
|
|
getv(from_object, ['function_calling_config']),
|
|
to_object,
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _PrebuiltVoiceConfig_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['voice_name']) is not None:
|
|
setv(to_object, ['voiceName'], getv(from_object, ['voice_name']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _VoiceConfig_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['prebuilt_voice_config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['prebuiltVoiceConfig'],
|
|
_PrebuiltVoiceConfig_to_vertex(
|
|
api_client, getv(from_object, ['prebuilt_voice_config']), to_object
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _SpeechConfig_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['voice_config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['voiceConfig'],
|
|
_VoiceConfig_to_vertex(
|
|
api_client, getv(from_object, ['voice_config']), to_object
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['language_code']) is not None:
|
|
setv(to_object, ['languageCode'], getv(from_object, ['language_code']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _ThinkingConfig_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['include_thoughts']) is not None:
|
|
setv(
|
|
to_object, ['includeThoughts'], getv(from_object, ['include_thoughts'])
|
|
)
|
|
|
|
if getv(from_object, ['thinking_budget']) is not None:
|
|
setv(to_object, ['thinkingBudget'], getv(from_object, ['thinking_budget']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _GenerateContentConfig_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
|
|
if getv(from_object, ['system_instruction']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['systemInstruction'],
|
|
_Content_to_vertex(
|
|
api_client,
|
|
t.t_content(api_client, getv(from_object, ['system_instruction'])),
|
|
to_object,
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['temperature']) is not None:
|
|
setv(to_object, ['temperature'], getv(from_object, ['temperature']))
|
|
|
|
if getv(from_object, ['top_p']) is not None:
|
|
setv(to_object, ['topP'], getv(from_object, ['top_p']))
|
|
|
|
if getv(from_object, ['top_k']) is not None:
|
|
setv(to_object, ['topK'], getv(from_object, ['top_k']))
|
|
|
|
if getv(from_object, ['candidate_count']) is not None:
|
|
setv(to_object, ['candidateCount'], getv(from_object, ['candidate_count']))
|
|
|
|
if getv(from_object, ['max_output_tokens']) is not None:
|
|
setv(
|
|
to_object, ['maxOutputTokens'], getv(from_object, ['max_output_tokens'])
|
|
)
|
|
|
|
if getv(from_object, ['stop_sequences']) is not None:
|
|
setv(to_object, ['stopSequences'], getv(from_object, ['stop_sequences']))
|
|
|
|
if getv(from_object, ['response_logprobs']) is not None:
|
|
setv(
|
|
to_object,
|
|
['responseLogprobs'],
|
|
getv(from_object, ['response_logprobs']),
|
|
)
|
|
|
|
if getv(from_object, ['logprobs']) is not None:
|
|
setv(to_object, ['logprobs'], getv(from_object, ['logprobs']))
|
|
|
|
if getv(from_object, ['presence_penalty']) is not None:
|
|
setv(
|
|
to_object, ['presencePenalty'], getv(from_object, ['presence_penalty'])
|
|
)
|
|
|
|
if getv(from_object, ['frequency_penalty']) is not None:
|
|
setv(
|
|
to_object,
|
|
['frequencyPenalty'],
|
|
getv(from_object, ['frequency_penalty']),
|
|
)
|
|
|
|
if getv(from_object, ['seed']) is not None:
|
|
setv(to_object, ['seed'], getv(from_object, ['seed']))
|
|
|
|
if getv(from_object, ['response_mime_type']) is not None:
|
|
setv(
|
|
to_object,
|
|
['responseMimeType'],
|
|
getv(from_object, ['response_mime_type']),
|
|
)
|
|
|
|
if getv(from_object, ['response_schema']) is not None:
|
|
setv(
|
|
to_object,
|
|
['responseSchema'],
|
|
_Schema_to_vertex(
|
|
api_client,
|
|
t.t_schema(api_client, getv(from_object, ['response_schema'])),
|
|
to_object,
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['routing_config']) is not None:
|
|
setv(to_object, ['routingConfig'], getv(from_object, ['routing_config']))
|
|
|
|
if getv(from_object, ['model_selection_config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['modelConfig'],
|
|
_ModelSelectionConfig_to_vertex(
|
|
api_client, getv(from_object, ['model_selection_config']), to_object
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['safety_settings']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['safetySettings'],
|
|
[
|
|
_SafetySetting_to_vertex(api_client, item, to_object)
|
|
for item in getv(from_object, ['safety_settings'])
|
|
],
|
|
)
|
|
|
|
if getv(from_object, ['tools']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['tools'],
|
|
[
|
|
_Tool_to_vertex(api_client, t.t_tool(api_client, item), to_object)
|
|
for item in t.t_tools(api_client, getv(from_object, ['tools']))
|
|
],
|
|
)
|
|
|
|
if getv(from_object, ['tool_config']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['toolConfig'],
|
|
_ToolConfig_to_vertex(
|
|
api_client, getv(from_object, ['tool_config']), to_object
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['labels']) is not None:
|
|
setv(parent_object, ['labels'], getv(from_object, ['labels']))
|
|
|
|
if getv(from_object, ['cached_content']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['cachedContent'],
|
|
t.t_cached_content_name(
|
|
api_client, getv(from_object, ['cached_content'])
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['response_modalities']) is not None:
|
|
setv(
|
|
to_object,
|
|
['responseModalities'],
|
|
getv(from_object, ['response_modalities']),
|
|
)
|
|
|
|
if getv(from_object, ['media_resolution']) is not None:
|
|
setv(
|
|
to_object, ['mediaResolution'], getv(from_object, ['media_resolution'])
|
|
)
|
|
|
|
if getv(from_object, ['speech_config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['speechConfig'],
|
|
_SpeechConfig_to_vertex(
|
|
api_client,
|
|
t.t_speech_config(api_client, getv(from_object, ['speech_config'])),
|
|
to_object,
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['audio_timestamp']) is not None:
|
|
setv(to_object, ['audioTimestamp'], getv(from_object, ['audio_timestamp']))
|
|
|
|
if getv(from_object, ['thinking_config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['thinkingConfig'],
|
|
_ThinkingConfig_to_vertex(
|
|
api_client, getv(from_object, ['thinking_config']), to_object
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _GenerateContentParameters_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['model']) is not None:
|
|
setv(
|
|
to_object,
|
|
['_url', 'model'],
|
|
t.t_model(api_client, getv(from_object, ['model'])),
|
|
)
|
|
|
|
if getv(from_object, ['contents']) is not None:
|
|
setv(
|
|
to_object,
|
|
['contents'],
|
|
[
|
|
_Content_to_vertex(api_client, item, to_object)
|
|
for item in t.t_contents(
|
|
api_client, getv(from_object, ['contents'])
|
|
)
|
|
],
|
|
)
|
|
|
|
if getv(from_object, ['config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['generationConfig'],
|
|
_GenerateContentConfig_to_vertex(
|
|
api_client, getv(from_object, ['config']), to_object
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _EmbedContentConfig_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
|
|
if getv(from_object, ['task_type']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['instances[]', 'task_type'],
|
|
getv(from_object, ['task_type']),
|
|
)
|
|
|
|
if getv(from_object, ['title']) is not None:
|
|
setv(parent_object, ['instances[]', 'title'], getv(from_object, ['title']))
|
|
|
|
if getv(from_object, ['output_dimensionality']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'outputDimensionality'],
|
|
getv(from_object, ['output_dimensionality']),
|
|
)
|
|
|
|
if getv(from_object, ['mime_type']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['instances[]', 'mimeType'],
|
|
getv(from_object, ['mime_type']),
|
|
)
|
|
|
|
if getv(from_object, ['auto_truncate']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'autoTruncate'],
|
|
getv(from_object, ['auto_truncate']),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _EmbedContentParameters_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['model']) is not None:
|
|
setv(
|
|
to_object,
|
|
['_url', 'model'],
|
|
t.t_model(api_client, getv(from_object, ['model'])),
|
|
)
|
|
|
|
if getv(from_object, ['contents']) is not None:
|
|
setv(
|
|
to_object,
|
|
['instances[]', 'content'],
|
|
t.t_contents_for_embed(api_client, getv(from_object, ['contents'])),
|
|
)
|
|
|
|
if getv(from_object, ['config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['config'],
|
|
_EmbedContentConfig_to_vertex(
|
|
api_client, getv(from_object, ['config']), to_object
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _GenerateImagesConfig_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
|
|
if getv(from_object, ['output_gcs_uri']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'storageUri'],
|
|
getv(from_object, ['output_gcs_uri']),
|
|
)
|
|
|
|
if getv(from_object, ['negative_prompt']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'negativePrompt'],
|
|
getv(from_object, ['negative_prompt']),
|
|
)
|
|
|
|
if getv(from_object, ['number_of_images']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'sampleCount'],
|
|
getv(from_object, ['number_of_images']),
|
|
)
|
|
|
|
if getv(from_object, ['aspect_ratio']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'aspectRatio'],
|
|
getv(from_object, ['aspect_ratio']),
|
|
)
|
|
|
|
if getv(from_object, ['guidance_scale']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'guidanceScale'],
|
|
getv(from_object, ['guidance_scale']),
|
|
)
|
|
|
|
if getv(from_object, ['seed']) is not None:
|
|
setv(parent_object, ['parameters', 'seed'], getv(from_object, ['seed']))
|
|
|
|
if getv(from_object, ['safety_filter_level']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'safetySetting'],
|
|
getv(from_object, ['safety_filter_level']),
|
|
)
|
|
|
|
if getv(from_object, ['person_generation']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'personGeneration'],
|
|
getv(from_object, ['person_generation']),
|
|
)
|
|
|
|
if getv(from_object, ['include_safety_attributes']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'includeSafetyAttributes'],
|
|
getv(from_object, ['include_safety_attributes']),
|
|
)
|
|
|
|
if getv(from_object, ['include_rai_reason']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'includeRaiReason'],
|
|
getv(from_object, ['include_rai_reason']),
|
|
)
|
|
|
|
if getv(from_object, ['language']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'language'],
|
|
getv(from_object, ['language']),
|
|
)
|
|
|
|
if getv(from_object, ['output_mime_type']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'outputOptions', 'mimeType'],
|
|
getv(from_object, ['output_mime_type']),
|
|
)
|
|
|
|
if getv(from_object, ['output_compression_quality']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'outputOptions', 'compressionQuality'],
|
|
getv(from_object, ['output_compression_quality']),
|
|
)
|
|
|
|
if getv(from_object, ['add_watermark']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'addWatermark'],
|
|
getv(from_object, ['add_watermark']),
|
|
)
|
|
|
|
if getv(from_object, ['enhance_prompt']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'enhancePrompt'],
|
|
getv(from_object, ['enhance_prompt']),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _GenerateImagesParameters_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['model']) is not None:
|
|
setv(
|
|
to_object,
|
|
['_url', 'model'],
|
|
t.t_model(api_client, getv(from_object, ['model'])),
|
|
)
|
|
|
|
if getv(from_object, ['prompt']) is not None:
|
|
setv(to_object, ['instances[0]', 'prompt'], getv(from_object, ['prompt']))
|
|
|
|
if getv(from_object, ['config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['config'],
|
|
_GenerateImagesConfig_to_vertex(
|
|
api_client, getv(from_object, ['config']), to_object
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _Image_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['gcs_uri']) is not None:
|
|
setv(to_object, ['gcsUri'], getv(from_object, ['gcs_uri']))
|
|
|
|
if getv(from_object, ['image_bytes']) is not None:
|
|
setv(
|
|
to_object,
|
|
['bytesBase64Encoded'],
|
|
t.t_bytes(api_client, getv(from_object, ['image_bytes'])),
|
|
)
|
|
|
|
if getv(from_object, ['mime_type']) is not None:
|
|
setv(to_object, ['mimeType'], getv(from_object, ['mime_type']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _MaskReferenceConfig_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['mask_mode']) is not None:
|
|
setv(to_object, ['maskMode'], getv(from_object, ['mask_mode']))
|
|
|
|
if getv(from_object, ['segmentation_classes']) is not None:
|
|
setv(
|
|
to_object, ['maskClasses'], getv(from_object, ['segmentation_classes'])
|
|
)
|
|
|
|
if getv(from_object, ['mask_dilation']) is not None:
|
|
setv(to_object, ['dilation'], getv(from_object, ['mask_dilation']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _ControlReferenceConfig_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['control_type']) is not None:
|
|
setv(to_object, ['controlType'], getv(from_object, ['control_type']))
|
|
|
|
if getv(from_object, ['enable_control_image_computation']) is not None:
|
|
setv(
|
|
to_object,
|
|
['computeControl'],
|
|
getv(from_object, ['enable_control_image_computation']),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _StyleReferenceConfig_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['style_description']) is not None:
|
|
setv(
|
|
to_object,
|
|
['styleDescription'],
|
|
getv(from_object, ['style_description']),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _SubjectReferenceConfig_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['subject_type']) is not None:
|
|
setv(to_object, ['subjectType'], getv(from_object, ['subject_type']))
|
|
|
|
if getv(from_object, ['subject_description']) is not None:
|
|
setv(
|
|
to_object,
|
|
['subjectDescription'],
|
|
getv(from_object, ['subject_description']),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _ReferenceImageAPI_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['reference_image']) is not None:
|
|
setv(
|
|
to_object,
|
|
['referenceImage'],
|
|
_Image_to_vertex(
|
|
api_client, getv(from_object, ['reference_image']), to_object
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['reference_id']) is not None:
|
|
setv(to_object, ['referenceId'], getv(from_object, ['reference_id']))
|
|
|
|
if getv(from_object, ['reference_type']) is not None:
|
|
setv(to_object, ['referenceType'], getv(from_object, ['reference_type']))
|
|
|
|
if getv(from_object, ['mask_image_config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['maskImageConfig'],
|
|
_MaskReferenceConfig_to_vertex(
|
|
api_client, getv(from_object, ['mask_image_config']), to_object
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['control_image_config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['controlImageConfig'],
|
|
_ControlReferenceConfig_to_vertex(
|
|
api_client, getv(from_object, ['control_image_config']), to_object
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['style_image_config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['styleImageConfig'],
|
|
_StyleReferenceConfig_to_vertex(
|
|
api_client, getv(from_object, ['style_image_config']), to_object
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['subject_image_config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['subjectImageConfig'],
|
|
_SubjectReferenceConfig_to_vertex(
|
|
api_client, getv(from_object, ['subject_image_config']), to_object
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _EditImageConfig_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
|
|
if getv(from_object, ['output_gcs_uri']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'storageUri'],
|
|
getv(from_object, ['output_gcs_uri']),
|
|
)
|
|
|
|
if getv(from_object, ['negative_prompt']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'negativePrompt'],
|
|
getv(from_object, ['negative_prompt']),
|
|
)
|
|
|
|
if getv(from_object, ['number_of_images']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'sampleCount'],
|
|
getv(from_object, ['number_of_images']),
|
|
)
|
|
|
|
if getv(from_object, ['aspect_ratio']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'aspectRatio'],
|
|
getv(from_object, ['aspect_ratio']),
|
|
)
|
|
|
|
if getv(from_object, ['guidance_scale']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'guidanceScale'],
|
|
getv(from_object, ['guidance_scale']),
|
|
)
|
|
|
|
if getv(from_object, ['seed']) is not None:
|
|
setv(parent_object, ['parameters', 'seed'], getv(from_object, ['seed']))
|
|
|
|
if getv(from_object, ['safety_filter_level']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'safetySetting'],
|
|
getv(from_object, ['safety_filter_level']),
|
|
)
|
|
|
|
if getv(from_object, ['person_generation']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'personGeneration'],
|
|
getv(from_object, ['person_generation']),
|
|
)
|
|
|
|
if getv(from_object, ['include_safety_attributes']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'includeSafetyAttributes'],
|
|
getv(from_object, ['include_safety_attributes']),
|
|
)
|
|
|
|
if getv(from_object, ['include_rai_reason']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'includeRaiReason'],
|
|
getv(from_object, ['include_rai_reason']),
|
|
)
|
|
|
|
if getv(from_object, ['language']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'language'],
|
|
getv(from_object, ['language']),
|
|
)
|
|
|
|
if getv(from_object, ['output_mime_type']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'outputOptions', 'mimeType'],
|
|
getv(from_object, ['output_mime_type']),
|
|
)
|
|
|
|
if getv(from_object, ['output_compression_quality']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'outputOptions', 'compressionQuality'],
|
|
getv(from_object, ['output_compression_quality']),
|
|
)
|
|
|
|
if getv(from_object, ['edit_mode']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'editMode'],
|
|
getv(from_object, ['edit_mode']),
|
|
)
|
|
|
|
if getv(from_object, ['base_steps']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'editConfig', 'baseSteps'],
|
|
getv(from_object, ['base_steps']),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _EditImageParameters_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['model']) is not None:
|
|
setv(
|
|
to_object,
|
|
['_url', 'model'],
|
|
t.t_model(api_client, getv(from_object, ['model'])),
|
|
)
|
|
|
|
if getv(from_object, ['prompt']) is not None:
|
|
setv(to_object, ['instances[0]', 'prompt'], getv(from_object, ['prompt']))
|
|
|
|
if getv(from_object, ['reference_images']) is not None:
|
|
setv(
|
|
to_object,
|
|
['instances[0]', 'referenceImages'],
|
|
[
|
|
_ReferenceImageAPI_to_vertex(api_client, item, to_object)
|
|
for item in getv(from_object, ['reference_images'])
|
|
],
|
|
)
|
|
|
|
if getv(from_object, ['config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['config'],
|
|
_EditImageConfig_to_vertex(
|
|
api_client, getv(from_object, ['config']), to_object
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _UpscaleImageAPIConfig_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
|
|
if getv(from_object, ['include_rai_reason']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'includeRaiReason'],
|
|
getv(from_object, ['include_rai_reason']),
|
|
)
|
|
|
|
if getv(from_object, ['output_mime_type']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'outputOptions', 'mimeType'],
|
|
getv(from_object, ['output_mime_type']),
|
|
)
|
|
|
|
if getv(from_object, ['output_compression_quality']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'outputOptions', 'compressionQuality'],
|
|
getv(from_object, ['output_compression_quality']),
|
|
)
|
|
|
|
if getv(from_object, ['number_of_images']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'sampleCount'],
|
|
getv(from_object, ['number_of_images']),
|
|
)
|
|
|
|
if getv(from_object, ['mode']) is not None:
|
|
setv(parent_object, ['parameters', 'mode'], getv(from_object, ['mode']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _UpscaleImageAPIParameters_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['model']) is not None:
|
|
setv(
|
|
to_object,
|
|
['_url', 'model'],
|
|
t.t_model(api_client, getv(from_object, ['model'])),
|
|
)
|
|
|
|
if getv(from_object, ['image']) is not None:
|
|
setv(
|
|
to_object,
|
|
['instances[0]', 'image'],
|
|
_Image_to_vertex(api_client, getv(from_object, ['image']), to_object),
|
|
)
|
|
|
|
if getv(from_object, ['upscale_factor']) is not None:
|
|
setv(
|
|
to_object,
|
|
['parameters', 'upscaleConfig', 'upscaleFactor'],
|
|
getv(from_object, ['upscale_factor']),
|
|
)
|
|
|
|
if getv(from_object, ['config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['config'],
|
|
_UpscaleImageAPIConfig_to_vertex(
|
|
api_client, getv(from_object, ['config']), to_object
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _GetModelParameters_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['model']) is not None:
|
|
setv(
|
|
to_object,
|
|
['_url', 'name'],
|
|
t.t_model(api_client, getv(from_object, ['model'])),
|
|
)
|
|
|
|
if getv(from_object, ['config']) is not None:
|
|
setv(to_object, ['config'], getv(from_object, ['config']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _ListModelsConfig_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
|
|
if getv(from_object, ['page_size']) is not None:
|
|
setv(
|
|
parent_object, ['_query', 'pageSize'], getv(from_object, ['page_size'])
|
|
)
|
|
|
|
if getv(from_object, ['page_token']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['_query', 'pageToken'],
|
|
getv(from_object, ['page_token']),
|
|
)
|
|
|
|
if getv(from_object, ['filter']) is not None:
|
|
setv(parent_object, ['_query', 'filter'], getv(from_object, ['filter']))
|
|
|
|
if getv(from_object, ['query_base']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['_url', 'models_url'],
|
|
t.t_models_url(api_client, getv(from_object, ['query_base'])),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _ListModelsParameters_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['config'],
|
|
_ListModelsConfig_to_vertex(
|
|
api_client, getv(from_object, ['config']), to_object
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _UpdateModelConfig_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
|
|
if getv(from_object, ['display_name']) is not None:
|
|
setv(parent_object, ['displayName'], getv(from_object, ['display_name']))
|
|
|
|
if getv(from_object, ['description']) is not None:
|
|
setv(parent_object, ['description'], getv(from_object, ['description']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _UpdateModelParameters_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['model']) is not None:
|
|
setv(
|
|
to_object,
|
|
['_url', 'model'],
|
|
t.t_model(api_client, getv(from_object, ['model'])),
|
|
)
|
|
|
|
if getv(from_object, ['config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['config'],
|
|
_UpdateModelConfig_to_vertex(
|
|
api_client, getv(from_object, ['config']), to_object
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _DeleteModelParameters_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['model']) is not None:
|
|
setv(
|
|
to_object,
|
|
['_url', 'name'],
|
|
t.t_model(api_client, getv(from_object, ['model'])),
|
|
)
|
|
|
|
if getv(from_object, ['config']) is not None:
|
|
setv(to_object, ['config'], getv(from_object, ['config']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _CountTokensConfig_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
|
|
if getv(from_object, ['system_instruction']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['systemInstruction'],
|
|
_Content_to_vertex(
|
|
api_client,
|
|
t.t_content(api_client, getv(from_object, ['system_instruction'])),
|
|
to_object,
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['tools']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['tools'],
|
|
[
|
|
_Tool_to_vertex(api_client, item, to_object)
|
|
for item in getv(from_object, ['tools'])
|
|
],
|
|
)
|
|
|
|
if getv(from_object, ['generation_config']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['generationConfig'],
|
|
getv(from_object, ['generation_config']),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _CountTokensParameters_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['model']) is not None:
|
|
setv(
|
|
to_object,
|
|
['_url', 'model'],
|
|
t.t_model(api_client, getv(from_object, ['model'])),
|
|
)
|
|
|
|
if getv(from_object, ['contents']) is not None:
|
|
setv(
|
|
to_object,
|
|
['contents'],
|
|
[
|
|
_Content_to_vertex(api_client, item, to_object)
|
|
for item in t.t_contents(
|
|
api_client, getv(from_object, ['contents'])
|
|
)
|
|
],
|
|
)
|
|
|
|
if getv(from_object, ['config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['config'],
|
|
_CountTokensConfig_to_vertex(
|
|
api_client, getv(from_object, ['config']), to_object
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _ComputeTokensParameters_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['model']) is not None:
|
|
setv(
|
|
to_object,
|
|
['_url', 'model'],
|
|
t.t_model(api_client, getv(from_object, ['model'])),
|
|
)
|
|
|
|
if getv(from_object, ['contents']) is not None:
|
|
setv(
|
|
to_object,
|
|
['contents'],
|
|
[
|
|
_Content_to_vertex(api_client, item, to_object)
|
|
for item in t.t_contents(
|
|
api_client, getv(from_object, ['contents'])
|
|
)
|
|
],
|
|
)
|
|
|
|
if getv(from_object, ['config']) is not None:
|
|
setv(to_object, ['config'], getv(from_object, ['config']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _GenerateVideosConfig_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
|
|
if getv(from_object, ['number_of_videos']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'sampleCount'],
|
|
getv(from_object, ['number_of_videos']),
|
|
)
|
|
|
|
if getv(from_object, ['output_gcs_uri']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'storageUri'],
|
|
getv(from_object, ['output_gcs_uri']),
|
|
)
|
|
|
|
if getv(from_object, ['fps']) is not None:
|
|
setv(parent_object, ['parameters', 'fps'], getv(from_object, ['fps']))
|
|
|
|
if getv(from_object, ['duration_seconds']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'durationSeconds'],
|
|
getv(from_object, ['duration_seconds']),
|
|
)
|
|
|
|
if getv(from_object, ['seed']) is not None:
|
|
setv(parent_object, ['parameters', 'seed'], getv(from_object, ['seed']))
|
|
|
|
if getv(from_object, ['aspect_ratio']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'aspectRatio'],
|
|
getv(from_object, ['aspect_ratio']),
|
|
)
|
|
|
|
if getv(from_object, ['resolution']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'resolution'],
|
|
getv(from_object, ['resolution']),
|
|
)
|
|
|
|
if getv(from_object, ['person_generation']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'personGeneration'],
|
|
getv(from_object, ['person_generation']),
|
|
)
|
|
|
|
if getv(from_object, ['pubsub_topic']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'pubsubTopic'],
|
|
getv(from_object, ['pubsub_topic']),
|
|
)
|
|
|
|
if getv(from_object, ['negative_prompt']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'negativePrompt'],
|
|
getv(from_object, ['negative_prompt']),
|
|
)
|
|
|
|
if getv(from_object, ['enhance_prompt']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['parameters', 'enhancePrompt'],
|
|
getv(from_object, ['enhance_prompt']),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _GenerateVideosParameters_to_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['model']) is not None:
|
|
setv(
|
|
to_object,
|
|
['_url', 'model'],
|
|
t.t_model(api_client, getv(from_object, ['model'])),
|
|
)
|
|
|
|
if getv(from_object, ['prompt']) is not None:
|
|
setv(to_object, ['instances[0]', 'prompt'], getv(from_object, ['prompt']))
|
|
|
|
if getv(from_object, ['image']) is not None:
|
|
setv(
|
|
to_object,
|
|
['instances[0]', 'image'],
|
|
_Image_to_vertex(api_client, getv(from_object, ['image']), to_object),
|
|
)
|
|
|
|
if getv(from_object, ['config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['config'],
|
|
_GenerateVideosConfig_to_vertex(
|
|
api_client, getv(from_object, ['config']), to_object
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _FeatureSelectionPreference_to_mldev_enum_validate(enum_value: Any) -> None:
|
|
if enum_value in set([
|
|
'FEATURE_SELECTION_PREFERENCE_UNSPECIFIED',
|
|
'PRIORITIZE_QUALITY',
|
|
'BALANCED',
|
|
'PRIORITIZE_COST',
|
|
]):
|
|
raise ValueError(f'{enum_value} enum value is not supported in Gemini API.')
|
|
|
|
|
|
def _SafetyFilterLevel_to_mldev_enum_validate(enum_value: Any) -> None:
|
|
if enum_value in set(['BLOCK_NONE']):
|
|
raise ValueError(f'{enum_value} enum value is not supported in Gemini API.')
|
|
|
|
|
|
def _PersonGeneration_to_mldev_enum_validate(enum_value: Any) -> None:
|
|
if enum_value in set(['ALLOW_ALL']):
|
|
raise ValueError(f'{enum_value} enum value is not supported in Gemini API.')
|
|
|
|
|
|
def _Part_from_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
|
|
if getv(from_object, ['thought']) is not None:
|
|
setv(to_object, ['thought'], getv(from_object, ['thought']))
|
|
|
|
if getv(from_object, ['codeExecutionResult']) is not None:
|
|
setv(
|
|
to_object,
|
|
['code_execution_result'],
|
|
getv(from_object, ['codeExecutionResult']),
|
|
)
|
|
|
|
if getv(from_object, ['executableCode']) is not None:
|
|
setv(to_object, ['executable_code'], getv(from_object, ['executableCode']))
|
|
|
|
if getv(from_object, ['fileData']) is not None:
|
|
setv(to_object, ['file_data'], getv(from_object, ['fileData']))
|
|
|
|
if getv(from_object, ['functionCall']) is not None:
|
|
setv(to_object, ['function_call'], getv(from_object, ['functionCall']))
|
|
|
|
if getv(from_object, ['functionResponse']) is not None:
|
|
setv(
|
|
to_object,
|
|
['function_response'],
|
|
getv(from_object, ['functionResponse']),
|
|
)
|
|
|
|
if getv(from_object, ['inlineData']) is not None:
|
|
setv(to_object, ['inline_data'], getv(from_object, ['inlineData']))
|
|
|
|
if getv(from_object, ['text']) is not None:
|
|
setv(to_object, ['text'], getv(from_object, ['text']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _Content_from_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['parts']) is not None:
|
|
setv(
|
|
to_object,
|
|
['parts'],
|
|
[
|
|
_Part_from_mldev(api_client, item, to_object)
|
|
for item in getv(from_object, ['parts'])
|
|
],
|
|
)
|
|
|
|
if getv(from_object, ['role']) is not None:
|
|
setv(to_object, ['role'], getv(from_object, ['role']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _CitationMetadata_from_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['citationSources']) is not None:
|
|
setv(to_object, ['citations'], getv(from_object, ['citationSources']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _Candidate_from_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['content']) is not None:
|
|
setv(
|
|
to_object,
|
|
['content'],
|
|
_Content_from_mldev(
|
|
api_client, getv(from_object, ['content']), to_object
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['citationMetadata']) is not None:
|
|
setv(
|
|
to_object,
|
|
['citation_metadata'],
|
|
_CitationMetadata_from_mldev(
|
|
api_client, getv(from_object, ['citationMetadata']), to_object
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['tokenCount']) is not None:
|
|
setv(to_object, ['token_count'], getv(from_object, ['tokenCount']))
|
|
|
|
if getv(from_object, ['finishReason']) is not None:
|
|
setv(to_object, ['finish_reason'], getv(from_object, ['finishReason']))
|
|
|
|
if getv(from_object, ['avgLogprobs']) is not None:
|
|
setv(to_object, ['avg_logprobs'], getv(from_object, ['avgLogprobs']))
|
|
|
|
if getv(from_object, ['groundingMetadata']) is not None:
|
|
setv(
|
|
to_object,
|
|
['grounding_metadata'],
|
|
getv(from_object, ['groundingMetadata']),
|
|
)
|
|
|
|
if getv(from_object, ['index']) is not None:
|
|
setv(to_object, ['index'], getv(from_object, ['index']))
|
|
|
|
if getv(from_object, ['logprobsResult']) is not None:
|
|
setv(to_object, ['logprobs_result'], getv(from_object, ['logprobsResult']))
|
|
|
|
if getv(from_object, ['safetyRatings']) is not None:
|
|
setv(to_object, ['safety_ratings'], getv(from_object, ['safetyRatings']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _GenerateContentResponse_from_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['candidates']) is not None:
|
|
setv(
|
|
to_object,
|
|
['candidates'],
|
|
[
|
|
_Candidate_from_mldev(api_client, item, to_object)
|
|
for item in getv(from_object, ['candidates'])
|
|
],
|
|
)
|
|
|
|
if getv(from_object, ['modelVersion']) is not None:
|
|
setv(to_object, ['model_version'], getv(from_object, ['modelVersion']))
|
|
|
|
if getv(from_object, ['promptFeedback']) is not None:
|
|
setv(to_object, ['prompt_feedback'], getv(from_object, ['promptFeedback']))
|
|
|
|
if getv(from_object, ['usageMetadata']) is not None:
|
|
setv(to_object, ['usage_metadata'], getv(from_object, ['usageMetadata']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _ContentEmbeddingStatistics_from_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
|
|
return to_object
|
|
|
|
|
|
def _ContentEmbedding_from_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['values']) is not None:
|
|
setv(to_object, ['values'], getv(from_object, ['values']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _EmbedContentMetadata_from_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
|
|
return to_object
|
|
|
|
|
|
def _EmbedContentResponse_from_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['embeddings']) is not None:
|
|
setv(
|
|
to_object,
|
|
['embeddings'],
|
|
[
|
|
_ContentEmbedding_from_mldev(api_client, item, to_object)
|
|
for item in getv(from_object, ['embeddings'])
|
|
],
|
|
)
|
|
|
|
if getv(from_object, ['metadata']) is not None:
|
|
setv(
|
|
to_object,
|
|
['metadata'],
|
|
_EmbedContentMetadata_from_mldev(
|
|
api_client, getv(from_object, ['metadata']), to_object
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _Image_from_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
|
|
if getv(from_object, ['bytesBase64Encoded']) is not None:
|
|
setv(
|
|
to_object,
|
|
['image_bytes'],
|
|
t.t_bytes(api_client, getv(from_object, ['bytesBase64Encoded'])),
|
|
)
|
|
|
|
if getv(from_object, ['mimeType']) is not None:
|
|
setv(to_object, ['mime_type'], getv(from_object, ['mimeType']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _SafetyAttributes_from_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['safetyAttributes', 'categories']) is not None:
|
|
setv(
|
|
to_object,
|
|
['categories'],
|
|
getv(from_object, ['safetyAttributes', 'categories']),
|
|
)
|
|
|
|
if getv(from_object, ['safetyAttributes', 'scores']) is not None:
|
|
setv(
|
|
to_object, ['scores'], getv(from_object, ['safetyAttributes', 'scores'])
|
|
)
|
|
|
|
if getv(from_object, ['contentType']) is not None:
|
|
setv(to_object, ['content_type'], getv(from_object, ['contentType']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _GeneratedImage_from_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['_self']) is not None:
|
|
setv(
|
|
to_object,
|
|
['image'],
|
|
_Image_from_mldev(api_client, getv(from_object, ['_self']), to_object),
|
|
)
|
|
|
|
if getv(from_object, ['raiFilteredReason']) is not None:
|
|
setv(
|
|
to_object,
|
|
['rai_filtered_reason'],
|
|
getv(from_object, ['raiFilteredReason']),
|
|
)
|
|
|
|
if getv(from_object, ['_self']) is not None:
|
|
setv(
|
|
to_object,
|
|
['safety_attributes'],
|
|
_SafetyAttributes_from_mldev(
|
|
api_client, getv(from_object, ['_self']), to_object
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _GenerateImagesResponse_from_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['predictions']) is not None:
|
|
setv(
|
|
to_object,
|
|
['generated_images'],
|
|
[
|
|
_GeneratedImage_from_mldev(api_client, item, to_object)
|
|
for item in getv(from_object, ['predictions'])
|
|
],
|
|
)
|
|
|
|
if getv(from_object, ['positivePromptSafetyAttributes']) is not None:
|
|
setv(
|
|
to_object,
|
|
['positive_prompt_safety_attributes'],
|
|
_SafetyAttributes_from_mldev(
|
|
api_client,
|
|
getv(from_object, ['positivePromptSafetyAttributes']),
|
|
to_object,
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _Endpoint_from_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
|
|
return to_object
|
|
|
|
|
|
def _TunedModelInfo_from_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['baseModel']) is not None:
|
|
setv(to_object, ['base_model'], getv(from_object, ['baseModel']))
|
|
|
|
if getv(from_object, ['createTime']) is not None:
|
|
setv(to_object, ['create_time'], getv(from_object, ['createTime']))
|
|
|
|
if getv(from_object, ['updateTime']) is not None:
|
|
setv(to_object, ['update_time'], getv(from_object, ['updateTime']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _Model_from_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['name']) is not None:
|
|
setv(to_object, ['name'], getv(from_object, ['name']))
|
|
|
|
if getv(from_object, ['displayName']) is not None:
|
|
setv(to_object, ['display_name'], getv(from_object, ['displayName']))
|
|
|
|
if getv(from_object, ['description']) is not None:
|
|
setv(to_object, ['description'], getv(from_object, ['description']))
|
|
|
|
if getv(from_object, ['version']) is not None:
|
|
setv(to_object, ['version'], getv(from_object, ['version']))
|
|
|
|
if getv(from_object, ['_self']) is not None:
|
|
setv(
|
|
to_object,
|
|
['tuned_model_info'],
|
|
_TunedModelInfo_from_mldev(
|
|
api_client, getv(from_object, ['_self']), to_object
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['inputTokenLimit']) is not None:
|
|
setv(
|
|
to_object, ['input_token_limit'], getv(from_object, ['inputTokenLimit'])
|
|
)
|
|
|
|
if getv(from_object, ['outputTokenLimit']) is not None:
|
|
setv(
|
|
to_object,
|
|
['output_token_limit'],
|
|
getv(from_object, ['outputTokenLimit']),
|
|
)
|
|
|
|
if getv(from_object, ['supportedGenerationMethods']) is not None:
|
|
setv(
|
|
to_object,
|
|
['supported_actions'],
|
|
getv(from_object, ['supportedGenerationMethods']),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _ListModelsResponse_from_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['nextPageToken']) is not None:
|
|
setv(to_object, ['next_page_token'], getv(from_object, ['nextPageToken']))
|
|
|
|
if getv(from_object, ['_self']) is not None:
|
|
setv(
|
|
to_object,
|
|
['models'],
|
|
[
|
|
_Model_from_mldev(api_client, item, to_object)
|
|
for item in t.t_extract_models(
|
|
api_client, getv(from_object, ['_self'])
|
|
)
|
|
],
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _DeleteModelResponse_from_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
|
|
return to_object
|
|
|
|
|
|
def _CountTokensResponse_from_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['totalTokens']) is not None:
|
|
setv(to_object, ['total_tokens'], getv(from_object, ['totalTokens']))
|
|
|
|
if getv(from_object, ['cachedContentTokenCount']) is not None:
|
|
setv(
|
|
to_object,
|
|
['cached_content_token_count'],
|
|
getv(from_object, ['cachedContentTokenCount']),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _Video_from_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['video', 'uri']) is not None:
|
|
setv(to_object, ['uri'], getv(from_object, ['video', 'uri']))
|
|
|
|
if getv(from_object, ['video', 'encodedVideo']) is not None:
|
|
setv(
|
|
to_object,
|
|
['video_bytes'],
|
|
t.t_bytes(api_client, getv(from_object, ['video', 'encodedVideo'])),
|
|
)
|
|
|
|
if getv(from_object, ['encoding']) is not None:
|
|
setv(to_object, ['mime_type'], getv(from_object, ['encoding']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _GeneratedVideo_from_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['_self']) is not None:
|
|
setv(
|
|
to_object,
|
|
['video'],
|
|
_Video_from_mldev(api_client, getv(from_object, ['_self']), to_object),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _GenerateVideosResponse_from_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['generatedSamples']) is not None:
|
|
setv(
|
|
to_object,
|
|
['generated_videos'],
|
|
[
|
|
_GeneratedVideo_from_mldev(api_client, item, to_object)
|
|
for item in getv(from_object, ['generatedSamples'])
|
|
],
|
|
)
|
|
|
|
if getv(from_object, ['raiMediaFilteredCount']) is not None:
|
|
setv(
|
|
to_object,
|
|
['rai_media_filtered_count'],
|
|
getv(from_object, ['raiMediaFilteredCount']),
|
|
)
|
|
|
|
if getv(from_object, ['raiMediaFilteredReasons']) is not None:
|
|
setv(
|
|
to_object,
|
|
['rai_media_filtered_reasons'],
|
|
getv(from_object, ['raiMediaFilteredReasons']),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _GenerateVideosOperation_from_mldev(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['name']) is not None:
|
|
setv(to_object, ['name'], getv(from_object, ['name']))
|
|
|
|
if getv(from_object, ['metadata']) is not None:
|
|
setv(to_object, ['metadata'], getv(from_object, ['metadata']))
|
|
|
|
if getv(from_object, ['done']) is not None:
|
|
setv(to_object, ['done'], getv(from_object, ['done']))
|
|
|
|
if getv(from_object, ['error']) is not None:
|
|
setv(to_object, ['error'], getv(from_object, ['error']))
|
|
|
|
if getv(from_object, ['response', 'generateVideoResponse']) is not None:
|
|
setv(
|
|
to_object,
|
|
['response'],
|
|
_GenerateVideosResponse_from_mldev(
|
|
api_client,
|
|
getv(from_object, ['response', 'generateVideoResponse']),
|
|
to_object,
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['response', 'generateVideoResponse']) is not None:
|
|
setv(
|
|
to_object,
|
|
['result'],
|
|
_GenerateVideosResponse_from_mldev(
|
|
api_client,
|
|
getv(from_object, ['response', 'generateVideoResponse']),
|
|
to_object,
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _Part_from_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['videoMetadata']) is not None:
|
|
setv(to_object, ['video_metadata'], getv(from_object, ['videoMetadata']))
|
|
|
|
if getv(from_object, ['thought']) is not None:
|
|
setv(to_object, ['thought'], getv(from_object, ['thought']))
|
|
|
|
if getv(from_object, ['codeExecutionResult']) is not None:
|
|
setv(
|
|
to_object,
|
|
['code_execution_result'],
|
|
getv(from_object, ['codeExecutionResult']),
|
|
)
|
|
|
|
if getv(from_object, ['executableCode']) is not None:
|
|
setv(to_object, ['executable_code'], getv(from_object, ['executableCode']))
|
|
|
|
if getv(from_object, ['fileData']) is not None:
|
|
setv(to_object, ['file_data'], getv(from_object, ['fileData']))
|
|
|
|
if getv(from_object, ['functionCall']) is not None:
|
|
setv(to_object, ['function_call'], getv(from_object, ['functionCall']))
|
|
|
|
if getv(from_object, ['functionResponse']) is not None:
|
|
setv(
|
|
to_object,
|
|
['function_response'],
|
|
getv(from_object, ['functionResponse']),
|
|
)
|
|
|
|
if getv(from_object, ['inlineData']) is not None:
|
|
setv(to_object, ['inline_data'], getv(from_object, ['inlineData']))
|
|
|
|
if getv(from_object, ['text']) is not None:
|
|
setv(to_object, ['text'], getv(from_object, ['text']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _Content_from_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['parts']) is not None:
|
|
setv(
|
|
to_object,
|
|
['parts'],
|
|
[
|
|
_Part_from_vertex(api_client, item, to_object)
|
|
for item in getv(from_object, ['parts'])
|
|
],
|
|
)
|
|
|
|
if getv(from_object, ['role']) is not None:
|
|
setv(to_object, ['role'], getv(from_object, ['role']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _CitationMetadata_from_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['citations']) is not None:
|
|
setv(to_object, ['citations'], getv(from_object, ['citations']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _Candidate_from_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['content']) is not None:
|
|
setv(
|
|
to_object,
|
|
['content'],
|
|
_Content_from_vertex(
|
|
api_client, getv(from_object, ['content']), to_object
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['citationMetadata']) is not None:
|
|
setv(
|
|
to_object,
|
|
['citation_metadata'],
|
|
_CitationMetadata_from_vertex(
|
|
api_client, getv(from_object, ['citationMetadata']), to_object
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['finishMessage']) is not None:
|
|
setv(to_object, ['finish_message'], getv(from_object, ['finishMessage']))
|
|
|
|
if getv(from_object, ['finishReason']) is not None:
|
|
setv(to_object, ['finish_reason'], getv(from_object, ['finishReason']))
|
|
|
|
if getv(from_object, ['avgLogprobs']) is not None:
|
|
setv(to_object, ['avg_logprobs'], getv(from_object, ['avgLogprobs']))
|
|
|
|
if getv(from_object, ['groundingMetadata']) is not None:
|
|
setv(
|
|
to_object,
|
|
['grounding_metadata'],
|
|
getv(from_object, ['groundingMetadata']),
|
|
)
|
|
|
|
if getv(from_object, ['index']) is not None:
|
|
setv(to_object, ['index'], getv(from_object, ['index']))
|
|
|
|
if getv(from_object, ['logprobsResult']) is not None:
|
|
setv(to_object, ['logprobs_result'], getv(from_object, ['logprobsResult']))
|
|
|
|
if getv(from_object, ['safetyRatings']) is not None:
|
|
setv(to_object, ['safety_ratings'], getv(from_object, ['safetyRatings']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _GenerateContentResponse_from_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['candidates']) is not None:
|
|
setv(
|
|
to_object,
|
|
['candidates'],
|
|
[
|
|
_Candidate_from_vertex(api_client, item, to_object)
|
|
for item in getv(from_object, ['candidates'])
|
|
],
|
|
)
|
|
|
|
if getv(from_object, ['createTime']) is not None:
|
|
setv(to_object, ['create_time'], getv(from_object, ['createTime']))
|
|
|
|
if getv(from_object, ['responseId']) is not None:
|
|
setv(to_object, ['response_id'], getv(from_object, ['responseId']))
|
|
|
|
if getv(from_object, ['modelVersion']) is not None:
|
|
setv(to_object, ['model_version'], getv(from_object, ['modelVersion']))
|
|
|
|
if getv(from_object, ['promptFeedback']) is not None:
|
|
setv(to_object, ['prompt_feedback'], getv(from_object, ['promptFeedback']))
|
|
|
|
if getv(from_object, ['usageMetadata']) is not None:
|
|
setv(to_object, ['usage_metadata'], getv(from_object, ['usageMetadata']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _ContentEmbeddingStatistics_from_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['truncated']) is not None:
|
|
setv(to_object, ['truncated'], getv(from_object, ['truncated']))
|
|
|
|
if getv(from_object, ['token_count']) is not None:
|
|
setv(to_object, ['token_count'], getv(from_object, ['token_count']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _ContentEmbedding_from_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['values']) is not None:
|
|
setv(to_object, ['values'], getv(from_object, ['values']))
|
|
|
|
if getv(from_object, ['statistics']) is not None:
|
|
setv(
|
|
to_object,
|
|
['statistics'],
|
|
_ContentEmbeddingStatistics_from_vertex(
|
|
api_client, getv(from_object, ['statistics']), to_object
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _EmbedContentMetadata_from_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['billableCharacterCount']) is not None:
|
|
setv(
|
|
to_object,
|
|
['billable_character_count'],
|
|
getv(from_object, ['billableCharacterCount']),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _EmbedContentResponse_from_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['predictions[]', 'embeddings']) is not None:
|
|
setv(
|
|
to_object,
|
|
['embeddings'],
|
|
[
|
|
_ContentEmbedding_from_vertex(api_client, item, to_object)
|
|
for item in getv(from_object, ['predictions[]', 'embeddings'])
|
|
],
|
|
)
|
|
|
|
if getv(from_object, ['metadata']) is not None:
|
|
setv(
|
|
to_object,
|
|
['metadata'],
|
|
_EmbedContentMetadata_from_vertex(
|
|
api_client, getv(from_object, ['metadata']), to_object
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _Image_from_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['gcsUri']) is not None:
|
|
setv(to_object, ['gcs_uri'], getv(from_object, ['gcsUri']))
|
|
|
|
if getv(from_object, ['bytesBase64Encoded']) is not None:
|
|
setv(
|
|
to_object,
|
|
['image_bytes'],
|
|
t.t_bytes(api_client, getv(from_object, ['bytesBase64Encoded'])),
|
|
)
|
|
|
|
if getv(from_object, ['mimeType']) is not None:
|
|
setv(to_object, ['mime_type'], getv(from_object, ['mimeType']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _SafetyAttributes_from_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['safetyAttributes', 'categories']) is not None:
|
|
setv(
|
|
to_object,
|
|
['categories'],
|
|
getv(from_object, ['safetyAttributes', 'categories']),
|
|
)
|
|
|
|
if getv(from_object, ['safetyAttributes', 'scores']) is not None:
|
|
setv(
|
|
to_object, ['scores'], getv(from_object, ['safetyAttributes', 'scores'])
|
|
)
|
|
|
|
if getv(from_object, ['contentType']) is not None:
|
|
setv(to_object, ['content_type'], getv(from_object, ['contentType']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _GeneratedImage_from_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['_self']) is not None:
|
|
setv(
|
|
to_object,
|
|
['image'],
|
|
_Image_from_vertex(api_client, getv(from_object, ['_self']), to_object),
|
|
)
|
|
|
|
if getv(from_object, ['raiFilteredReason']) is not None:
|
|
setv(
|
|
to_object,
|
|
['rai_filtered_reason'],
|
|
getv(from_object, ['raiFilteredReason']),
|
|
)
|
|
|
|
if getv(from_object, ['_self']) is not None:
|
|
setv(
|
|
to_object,
|
|
['safety_attributes'],
|
|
_SafetyAttributes_from_vertex(
|
|
api_client, getv(from_object, ['_self']), to_object
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['prompt']) is not None:
|
|
setv(to_object, ['enhanced_prompt'], getv(from_object, ['prompt']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _GenerateImagesResponse_from_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['predictions']) is not None:
|
|
setv(
|
|
to_object,
|
|
['generated_images'],
|
|
[
|
|
_GeneratedImage_from_vertex(api_client, item, to_object)
|
|
for item in getv(from_object, ['predictions'])
|
|
],
|
|
)
|
|
|
|
if getv(from_object, ['positivePromptSafetyAttributes']) is not None:
|
|
setv(
|
|
to_object,
|
|
['positive_prompt_safety_attributes'],
|
|
_SafetyAttributes_from_vertex(
|
|
api_client,
|
|
getv(from_object, ['positivePromptSafetyAttributes']),
|
|
to_object,
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _EditImageResponse_from_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['predictions']) is not None:
|
|
setv(
|
|
to_object,
|
|
['generated_images'],
|
|
[
|
|
_GeneratedImage_from_vertex(api_client, item, to_object)
|
|
for item in getv(from_object, ['predictions'])
|
|
],
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _UpscaleImageResponse_from_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['predictions']) is not None:
|
|
setv(
|
|
to_object,
|
|
['generated_images'],
|
|
[
|
|
_GeneratedImage_from_vertex(api_client, item, to_object)
|
|
for item in getv(from_object, ['predictions'])
|
|
],
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _Endpoint_from_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['endpoint']) is not None:
|
|
setv(to_object, ['name'], getv(from_object, ['endpoint']))
|
|
|
|
if getv(from_object, ['deployedModelId']) is not None:
|
|
setv(
|
|
to_object, ['deployed_model_id'], getv(from_object, ['deployedModelId'])
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _TunedModelInfo_from_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if (
|
|
getv(from_object, ['labels', 'google-vertex-llm-tuning-base-model-id'])
|
|
is not None
|
|
):
|
|
setv(
|
|
to_object,
|
|
['base_model'],
|
|
getv(from_object, ['labels', 'google-vertex-llm-tuning-base-model-id']),
|
|
)
|
|
|
|
if getv(from_object, ['createTime']) is not None:
|
|
setv(to_object, ['create_time'], getv(from_object, ['createTime']))
|
|
|
|
if getv(from_object, ['updateTime']) is not None:
|
|
setv(to_object, ['update_time'], getv(from_object, ['updateTime']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _Model_from_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['name']) is not None:
|
|
setv(to_object, ['name'], getv(from_object, ['name']))
|
|
|
|
if getv(from_object, ['displayName']) is not None:
|
|
setv(to_object, ['display_name'], getv(from_object, ['displayName']))
|
|
|
|
if getv(from_object, ['description']) is not None:
|
|
setv(to_object, ['description'], getv(from_object, ['description']))
|
|
|
|
if getv(from_object, ['versionId']) is not None:
|
|
setv(to_object, ['version'], getv(from_object, ['versionId']))
|
|
|
|
if getv(from_object, ['deployedModels']) is not None:
|
|
setv(
|
|
to_object,
|
|
['endpoints'],
|
|
[
|
|
_Endpoint_from_vertex(api_client, item, to_object)
|
|
for item in getv(from_object, ['deployedModels'])
|
|
],
|
|
)
|
|
|
|
if getv(from_object, ['labels']) is not None:
|
|
setv(to_object, ['labels'], getv(from_object, ['labels']))
|
|
|
|
if getv(from_object, ['_self']) is not None:
|
|
setv(
|
|
to_object,
|
|
['tuned_model_info'],
|
|
_TunedModelInfo_from_vertex(
|
|
api_client, getv(from_object, ['_self']), to_object
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _ListModelsResponse_from_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['nextPageToken']) is not None:
|
|
setv(to_object, ['next_page_token'], getv(from_object, ['nextPageToken']))
|
|
|
|
if getv(from_object, ['_self']) is not None:
|
|
setv(
|
|
to_object,
|
|
['models'],
|
|
[
|
|
_Model_from_vertex(api_client, item, to_object)
|
|
for item in t.t_extract_models(
|
|
api_client, getv(from_object, ['_self'])
|
|
)
|
|
],
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _DeleteModelResponse_from_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
|
|
return to_object
|
|
|
|
|
|
def _CountTokensResponse_from_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['totalTokens']) is not None:
|
|
setv(to_object, ['total_tokens'], getv(from_object, ['totalTokens']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _ComputeTokensResponse_from_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['tokensInfo']) is not None:
|
|
setv(to_object, ['tokens_info'], getv(from_object, ['tokensInfo']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _Video_from_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['gcsUri']) is not None:
|
|
setv(to_object, ['uri'], getv(from_object, ['gcsUri']))
|
|
|
|
if getv(from_object, ['bytesBase64Encoded']) is not None:
|
|
setv(
|
|
to_object,
|
|
['video_bytes'],
|
|
t.t_bytes(api_client, getv(from_object, ['bytesBase64Encoded'])),
|
|
)
|
|
|
|
if getv(from_object, ['mimeType']) is not None:
|
|
setv(to_object, ['mime_type'], getv(from_object, ['mimeType']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _GeneratedVideo_from_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['_self']) is not None:
|
|
setv(
|
|
to_object,
|
|
['video'],
|
|
_Video_from_vertex(api_client, getv(from_object, ['_self']), to_object),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _GenerateVideosResponse_from_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['videos']) is not None:
|
|
setv(
|
|
to_object,
|
|
['generated_videos'],
|
|
[
|
|
_GeneratedVideo_from_vertex(api_client, item, to_object)
|
|
for item in getv(from_object, ['videos'])
|
|
],
|
|
)
|
|
|
|
if getv(from_object, ['raiMediaFilteredCount']) is not None:
|
|
setv(
|
|
to_object,
|
|
['rai_media_filtered_count'],
|
|
getv(from_object, ['raiMediaFilteredCount']),
|
|
)
|
|
|
|
if getv(from_object, ['raiMediaFilteredReasons']) is not None:
|
|
setv(
|
|
to_object,
|
|
['rai_media_filtered_reasons'],
|
|
getv(from_object, ['raiMediaFilteredReasons']),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _GenerateVideosOperation_from_vertex(
|
|
api_client: BaseApiClient,
|
|
from_object: Union[dict[str, Any], object],
|
|
parent_object: Optional[dict[str, Any]] = None,
|
|
) -> dict[str, Any]:
|
|
to_object: dict[str, Any] = {}
|
|
if getv(from_object, ['name']) is not None:
|
|
setv(to_object, ['name'], getv(from_object, ['name']))
|
|
|
|
if getv(from_object, ['metadata']) is not None:
|
|
setv(to_object, ['metadata'], getv(from_object, ['metadata']))
|
|
|
|
if getv(from_object, ['done']) is not None:
|
|
setv(to_object, ['done'], getv(from_object, ['done']))
|
|
|
|
if getv(from_object, ['error']) is not None:
|
|
setv(to_object, ['error'], getv(from_object, ['error']))
|
|
|
|
if getv(from_object, ['response']) is not None:
|
|
setv(
|
|
to_object,
|
|
['response'],
|
|
_GenerateVideosResponse_from_vertex(
|
|
api_client, getv(from_object, ['response']), to_object
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['response']) is not None:
|
|
setv(
|
|
to_object,
|
|
['result'],
|
|
_GenerateVideosResponse_from_vertex(
|
|
api_client, getv(from_object, ['response']), to_object
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
class Models(_api_module.BaseModule):
|
|
|
|
def _generate_content(
|
|
self,
|
|
*,
|
|
model: str,
|
|
contents: Union[types.ContentListUnion, types.ContentListUnionDict],
|
|
config: Optional[types.GenerateContentConfigOrDict] = None,
|
|
) -> types.GenerateContentResponse:
|
|
parameter_model = types._GenerateContentParameters(
|
|
model=model,
|
|
contents=contents,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _GenerateContentParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:generateContent'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:generateContent'
|
|
else:
|
|
request_dict = _GenerateContentParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:generateContent'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:generateContent'
|
|
query_params = request_dict.get('_query')
|
|
if query_params:
|
|
path = f'{path}?{urlencode(query_params)}'
|
|
# TODO: remove the hack that pops config.
|
|
request_dict.pop('config', None)
|
|
|
|
http_options: Optional[types.HttpOptions] = None
|
|
if (
|
|
parameter_model.config is not None
|
|
and parameter_model.config.http_options is not None
|
|
):
|
|
http_options = parameter_model.config.http_options
|
|
|
|
request_dict = _common.convert_to_dict(request_dict)
|
|
request_dict = _common.encode_unserializable_types(request_dict)
|
|
|
|
response_dict = self._api_client.request(
|
|
'post', path, request_dict, http_options
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _GenerateContentResponse_from_vertex(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
else:
|
|
response_dict = _GenerateContentResponse_from_mldev(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
return_value = types.GenerateContentResponse._from_response(
|
|
response=response_dict, kwargs=parameter_model.model_dump()
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
def _generate_content_stream(
|
|
self,
|
|
*,
|
|
model: str,
|
|
contents: Union[types.ContentListUnion, types.ContentListUnionDict],
|
|
config: Optional[types.GenerateContentConfigOrDict] = None,
|
|
) -> Iterator[types.GenerateContentResponse]:
|
|
parameter_model = types._GenerateContentParameters(
|
|
model=model,
|
|
contents=contents,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _GenerateContentParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:streamGenerateContent?alt=sse'.format_map(
|
|
request_url_dict
|
|
)
|
|
else:
|
|
path = '{model}:streamGenerateContent?alt=sse'
|
|
else:
|
|
request_dict = _GenerateContentParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:streamGenerateContent?alt=sse'.format_map(
|
|
request_url_dict
|
|
)
|
|
else:
|
|
path = '{model}:streamGenerateContent?alt=sse'
|
|
query_params = request_dict.get('_query')
|
|
if query_params:
|
|
path = f'{path}?{urlencode(query_params)}'
|
|
# TODO: remove the hack that pops config.
|
|
request_dict.pop('config', None)
|
|
|
|
http_options: Optional[types.HttpOptions] = None
|
|
if (
|
|
parameter_model.config is not None
|
|
and parameter_model.config.http_options is not None
|
|
):
|
|
http_options = parameter_model.config.http_options
|
|
|
|
request_dict = _common.convert_to_dict(request_dict)
|
|
request_dict = _common.encode_unserializable_types(request_dict)
|
|
|
|
for response_dict in self._api_client.request_streamed(
|
|
'post', path, request_dict, http_options
|
|
):
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _GenerateContentResponse_from_vertex(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
else:
|
|
response_dict = _GenerateContentResponse_from_mldev(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
return_value = types.GenerateContentResponse._from_response(
|
|
response=response_dict, kwargs=parameter_model.model_dump()
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
yield return_value
|
|
|
|
def embed_content(
|
|
self,
|
|
*,
|
|
model: str,
|
|
contents: Union[types.ContentListUnion, types.ContentListUnionDict],
|
|
config: Optional[types.EmbedContentConfigOrDict] = None,
|
|
) -> types.EmbedContentResponse:
|
|
"""Calculates embeddings for the given contents. Only text is supported.
|
|
|
|
Args:
|
|
model (str): The model to use.
|
|
contents (list[Content]): The contents to embed.
|
|
config (EmbedContentConfig): Optional configuration for embeddings.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
embeddings = client.models.embed_content(
|
|
model= 'text-embedding-004',
|
|
contents=[
|
|
'What is your name?',
|
|
'What is your favorite color?',
|
|
],
|
|
config={
|
|
'output_dimensionality': 64
|
|
},
|
|
)
|
|
"""
|
|
|
|
parameter_model = types._EmbedContentParameters(
|
|
model=model,
|
|
contents=contents,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _EmbedContentParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:predict'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:predict'
|
|
else:
|
|
request_dict = _EmbedContentParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:batchEmbedContents'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:batchEmbedContents'
|
|
query_params = request_dict.get('_query')
|
|
if query_params:
|
|
path = f'{path}?{urlencode(query_params)}'
|
|
# TODO: remove the hack that pops config.
|
|
request_dict.pop('config', None)
|
|
|
|
http_options: Optional[types.HttpOptions] = None
|
|
if (
|
|
parameter_model.config is not None
|
|
and parameter_model.config.http_options is not None
|
|
):
|
|
http_options = parameter_model.config.http_options
|
|
|
|
request_dict = _common.convert_to_dict(request_dict)
|
|
request_dict = _common.encode_unserializable_types(request_dict)
|
|
|
|
response_dict = self._api_client.request(
|
|
'post', path, request_dict, http_options
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _EmbedContentResponse_from_vertex(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
else:
|
|
response_dict = _EmbedContentResponse_from_mldev(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
return_value = types.EmbedContentResponse._from_response(
|
|
response=response_dict, kwargs=parameter_model.model_dump()
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
def _generate_images(
|
|
self,
|
|
*,
|
|
model: str,
|
|
prompt: str,
|
|
config: Optional[types.GenerateImagesConfigOrDict] = None,
|
|
) -> types.GenerateImagesResponse:
|
|
"""Generates images based on a text description and configuration.
|
|
|
|
Args:
|
|
model (str): The model to use.
|
|
prompt (str): A text description of the images to generate.
|
|
config (GenerateImagesConfig): Configuration for generation.
|
|
"""
|
|
|
|
parameter_model = types._GenerateImagesParameters(
|
|
model=model,
|
|
prompt=prompt,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _GenerateImagesParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:predict'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:predict'
|
|
else:
|
|
request_dict = _GenerateImagesParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:predict'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:predict'
|
|
query_params = request_dict.get('_query')
|
|
if query_params:
|
|
path = f'{path}?{urlencode(query_params)}'
|
|
# TODO: remove the hack that pops config.
|
|
request_dict.pop('config', None)
|
|
|
|
http_options: Optional[types.HttpOptions] = None
|
|
if (
|
|
parameter_model.config is not None
|
|
and parameter_model.config.http_options is not None
|
|
):
|
|
http_options = parameter_model.config.http_options
|
|
|
|
request_dict = _common.convert_to_dict(request_dict)
|
|
request_dict = _common.encode_unserializable_types(request_dict)
|
|
|
|
response_dict = self._api_client.request(
|
|
'post', path, request_dict, http_options
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _GenerateImagesResponse_from_vertex(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
else:
|
|
response_dict = _GenerateImagesResponse_from_mldev(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
return_value = types.GenerateImagesResponse._from_response(
|
|
response=response_dict, kwargs=parameter_model.model_dump()
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
def _edit_image(
|
|
self,
|
|
*,
|
|
model: str,
|
|
prompt: str,
|
|
reference_images: list[types._ReferenceImageAPIOrDict],
|
|
config: Optional[types.EditImageConfigOrDict] = None,
|
|
) -> types.EditImageResponse:
|
|
"""Edits an image based on a text description and configuration.
|
|
|
|
Args:
|
|
model (str): The model to use.
|
|
prompt (str): A text description of the edit to apply to the image.
|
|
reference_images (list[Union[RawReferenceImage, MaskReferenceImage,
|
|
ControlReferenceImage, StyleReferenceImage, SubjectReferenceImage]): The
|
|
reference images for editing.
|
|
config (EditImageConfig): Configuration for editing.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
from google.genai.types import RawReferenceImage, MaskReferenceImage
|
|
|
|
raw_ref_image = RawReferenceImage(
|
|
reference_id=1,
|
|
reference_image=types.Image.from_file(IMAGE_FILE_PATH),
|
|
)
|
|
|
|
mask_ref_image = MaskReferenceImage(
|
|
reference_id=2,
|
|
config=types.MaskReferenceConfig(
|
|
mask_mode='MASK_MODE_FOREGROUND',
|
|
mask_dilation=0.06,
|
|
),
|
|
)
|
|
response = client.models.edit_image(
|
|
model='imagen-3.0-capability-001',
|
|
prompt='man with dog',
|
|
reference_images=[raw_ref_image, mask_ref_image],
|
|
config=types.EditImageConfig(
|
|
edit_mode= "EDIT_MODE_INPAINT_INSERTION",
|
|
number_of_images= 1,
|
|
include_rai_reason= True,
|
|
)
|
|
)
|
|
response.generated_images[0].image.show()
|
|
# Shows a man with a dog instead of a cat.
|
|
"""
|
|
|
|
parameter_model = types._EditImageParameters(
|
|
model=model,
|
|
prompt=prompt,
|
|
reference_images=reference_images,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
if not self._api_client.vertexai:
|
|
raise ValueError('This method is only supported in the Vertex AI client.')
|
|
else:
|
|
request_dict = _EditImageParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:predict'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:predict'
|
|
|
|
query_params = request_dict.get('_query')
|
|
if query_params:
|
|
path = f'{path}?{urlencode(query_params)}'
|
|
# TODO: remove the hack that pops config.
|
|
request_dict.pop('config', None)
|
|
|
|
http_options: Optional[types.HttpOptions] = None
|
|
if (
|
|
parameter_model.config is not None
|
|
and parameter_model.config.http_options is not None
|
|
):
|
|
http_options = parameter_model.config.http_options
|
|
|
|
request_dict = _common.convert_to_dict(request_dict)
|
|
request_dict = _common.encode_unserializable_types(request_dict)
|
|
|
|
response_dict = self._api_client.request(
|
|
'post', path, request_dict, http_options
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _EditImageResponse_from_vertex(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
return_value = types.EditImageResponse._from_response(
|
|
response=response_dict, kwargs=parameter_model.model_dump()
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
def _upscale_image(
|
|
self,
|
|
*,
|
|
model: str,
|
|
image: types.ImageOrDict,
|
|
upscale_factor: str,
|
|
config: Optional[types._UpscaleImageAPIConfigOrDict] = None,
|
|
) -> types.UpscaleImageResponse:
|
|
"""Upscales an image.
|
|
|
|
Args:
|
|
model (str): The model to use.
|
|
image (Image): The input image for upscaling.
|
|
upscale_factor (str): The factor to upscale the image (x2 or x4).
|
|
config (_UpscaleImageAPIConfig): Configuration for upscaling.
|
|
"""
|
|
|
|
parameter_model = types._UpscaleImageAPIParameters(
|
|
model=model,
|
|
image=image,
|
|
upscale_factor=upscale_factor,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
if not self._api_client.vertexai:
|
|
raise ValueError('This method is only supported in the Vertex AI client.')
|
|
else:
|
|
request_dict = _UpscaleImageAPIParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:predict'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:predict'
|
|
|
|
query_params = request_dict.get('_query')
|
|
if query_params:
|
|
path = f'{path}?{urlencode(query_params)}'
|
|
# TODO: remove the hack that pops config.
|
|
request_dict.pop('config', None)
|
|
|
|
http_options: Optional[types.HttpOptions] = None
|
|
if (
|
|
parameter_model.config is not None
|
|
and parameter_model.config.http_options is not None
|
|
):
|
|
http_options = parameter_model.config.http_options
|
|
|
|
request_dict = _common.convert_to_dict(request_dict)
|
|
request_dict = _common.encode_unserializable_types(request_dict)
|
|
|
|
response_dict = self._api_client.request(
|
|
'post', path, request_dict, http_options
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _UpscaleImageResponse_from_vertex(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
return_value = types.UpscaleImageResponse._from_response(
|
|
response=response_dict, kwargs=parameter_model.model_dump()
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
def get(
|
|
self, *, model: str, config: Optional[types.GetModelConfigOrDict] = None
|
|
) -> types.Model:
|
|
parameter_model = types._GetModelParameters(
|
|
model=model,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _GetModelParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{name}'.format_map(request_url_dict)
|
|
else:
|
|
path = '{name}'
|
|
else:
|
|
request_dict = _GetModelParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{name}'.format_map(request_url_dict)
|
|
else:
|
|
path = '{name}'
|
|
query_params = request_dict.get('_query')
|
|
if query_params:
|
|
path = f'{path}?{urlencode(query_params)}'
|
|
# TODO: remove the hack that pops config.
|
|
request_dict.pop('config', None)
|
|
|
|
http_options: Optional[types.HttpOptions] = None
|
|
if (
|
|
parameter_model.config is not None
|
|
and parameter_model.config.http_options is not None
|
|
):
|
|
http_options = parameter_model.config.http_options
|
|
|
|
request_dict = _common.convert_to_dict(request_dict)
|
|
request_dict = _common.encode_unserializable_types(request_dict)
|
|
|
|
response_dict = self._api_client.request(
|
|
'get', path, request_dict, http_options
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _Model_from_vertex(self._api_client, response_dict)
|
|
|
|
else:
|
|
response_dict = _Model_from_mldev(self._api_client, response_dict)
|
|
|
|
return_value = types.Model._from_response(
|
|
response=response_dict, kwargs=parameter_model.model_dump()
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
def _list(
|
|
self, *, config: Optional[types.ListModelsConfigOrDict] = None
|
|
) -> types.ListModelsResponse:
|
|
parameter_model = types._ListModelsParameters(
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _ListModelsParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{models_url}'.format_map(request_url_dict)
|
|
else:
|
|
path = '{models_url}'
|
|
else:
|
|
request_dict = _ListModelsParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{models_url}'.format_map(request_url_dict)
|
|
else:
|
|
path = '{models_url}'
|
|
query_params = request_dict.get('_query')
|
|
if query_params:
|
|
path = f'{path}?{urlencode(query_params)}'
|
|
# TODO: remove the hack that pops config.
|
|
request_dict.pop('config', None)
|
|
|
|
http_options: Optional[types.HttpOptions] = None
|
|
if (
|
|
parameter_model.config is not None
|
|
and parameter_model.config.http_options is not None
|
|
):
|
|
http_options = parameter_model.config.http_options
|
|
|
|
request_dict = _common.convert_to_dict(request_dict)
|
|
request_dict = _common.encode_unserializable_types(request_dict)
|
|
|
|
response_dict = self._api_client.request(
|
|
'get', path, request_dict, http_options
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _ListModelsResponse_from_vertex(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
else:
|
|
response_dict = _ListModelsResponse_from_mldev(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
return_value = types.ListModelsResponse._from_response(
|
|
response=response_dict, kwargs=parameter_model.model_dump()
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
def update(
|
|
self,
|
|
*,
|
|
model: str,
|
|
config: Optional[types.UpdateModelConfigOrDict] = None,
|
|
) -> types.Model:
|
|
parameter_model = types._UpdateModelParameters(
|
|
model=model,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _UpdateModelParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}'
|
|
else:
|
|
request_dict = _UpdateModelParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{name}'.format_map(request_url_dict)
|
|
else:
|
|
path = '{name}'
|
|
query_params = request_dict.get('_query')
|
|
if query_params:
|
|
path = f'{path}?{urlencode(query_params)}'
|
|
# TODO: remove the hack that pops config.
|
|
request_dict.pop('config', None)
|
|
|
|
http_options: Optional[types.HttpOptions] = None
|
|
if (
|
|
parameter_model.config is not None
|
|
and parameter_model.config.http_options is not None
|
|
):
|
|
http_options = parameter_model.config.http_options
|
|
|
|
request_dict = _common.convert_to_dict(request_dict)
|
|
request_dict = _common.encode_unserializable_types(request_dict)
|
|
|
|
response_dict = self._api_client.request(
|
|
'patch', path, request_dict, http_options
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _Model_from_vertex(self._api_client, response_dict)
|
|
|
|
else:
|
|
response_dict = _Model_from_mldev(self._api_client, response_dict)
|
|
|
|
return_value = types.Model._from_response(
|
|
response=response_dict, kwargs=parameter_model.model_dump()
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
def delete(
|
|
self,
|
|
*,
|
|
model: str,
|
|
config: Optional[types.DeleteModelConfigOrDict] = None,
|
|
) -> types.DeleteModelResponse:
|
|
parameter_model = types._DeleteModelParameters(
|
|
model=model,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _DeleteModelParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{name}'.format_map(request_url_dict)
|
|
else:
|
|
path = '{name}'
|
|
else:
|
|
request_dict = _DeleteModelParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{name}'.format_map(request_url_dict)
|
|
else:
|
|
path = '{name}'
|
|
query_params = request_dict.get('_query')
|
|
if query_params:
|
|
path = f'{path}?{urlencode(query_params)}'
|
|
# TODO: remove the hack that pops config.
|
|
request_dict.pop('config', None)
|
|
|
|
http_options: Optional[types.HttpOptions] = None
|
|
if (
|
|
parameter_model.config is not None
|
|
and parameter_model.config.http_options is not None
|
|
):
|
|
http_options = parameter_model.config.http_options
|
|
|
|
request_dict = _common.convert_to_dict(request_dict)
|
|
request_dict = _common.encode_unserializable_types(request_dict)
|
|
|
|
response_dict = self._api_client.request(
|
|
'delete', path, request_dict, http_options
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _DeleteModelResponse_from_vertex(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
else:
|
|
response_dict = _DeleteModelResponse_from_mldev(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
return_value = types.DeleteModelResponse._from_response(
|
|
response=response_dict, kwargs=parameter_model.model_dump()
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
def count_tokens(
|
|
self,
|
|
*,
|
|
model: str,
|
|
contents: Union[types.ContentListUnion, types.ContentListUnionDict],
|
|
config: Optional[types.CountTokensConfigOrDict] = None,
|
|
) -> types.CountTokensResponse:
|
|
"""Counts the number of tokens in the given content.
|
|
|
|
Multimodal input is supported for Gemini models.
|
|
|
|
Args:
|
|
model (str): The model to use for counting tokens.
|
|
contents (list[types.Content]): The content to count tokens for.
|
|
config (CountTokensConfig): The configuration for counting tokens.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
response = client.models.count_tokens(
|
|
model='gemini-2.0-flash',
|
|
contents='What is your name?',
|
|
)
|
|
print(response)
|
|
# total_tokens=5 cached_content_token_count=None
|
|
"""
|
|
|
|
parameter_model = types._CountTokensParameters(
|
|
model=model,
|
|
contents=contents,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _CountTokensParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:countTokens'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:countTokens'
|
|
else:
|
|
request_dict = _CountTokensParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:countTokens'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:countTokens'
|
|
query_params = request_dict.get('_query')
|
|
if query_params:
|
|
path = f'{path}?{urlencode(query_params)}'
|
|
# TODO: remove the hack that pops config.
|
|
request_dict.pop('config', None)
|
|
|
|
http_options: Optional[types.HttpOptions] = None
|
|
if (
|
|
parameter_model.config is not None
|
|
and parameter_model.config.http_options is not None
|
|
):
|
|
http_options = parameter_model.config.http_options
|
|
|
|
request_dict = _common.convert_to_dict(request_dict)
|
|
request_dict = _common.encode_unserializable_types(request_dict)
|
|
|
|
response_dict = self._api_client.request(
|
|
'post', path, request_dict, http_options
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _CountTokensResponse_from_vertex(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
else:
|
|
response_dict = _CountTokensResponse_from_mldev(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
return_value = types.CountTokensResponse._from_response(
|
|
response=response_dict, kwargs=parameter_model.model_dump()
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
def compute_tokens(
|
|
self,
|
|
*,
|
|
model: str,
|
|
contents: Union[types.ContentListUnion, types.ContentListUnionDict],
|
|
config: Optional[types.ComputeTokensConfigOrDict] = None,
|
|
) -> types.ComputeTokensResponse:
|
|
"""Given a list of contents, returns a corresponding TokensInfo containing the
|
|
|
|
list of tokens and list of token ids.
|
|
|
|
This method is not supported by the Gemini Developer API.
|
|
|
|
Args:
|
|
model (str): The model to use.
|
|
contents (list[shared.Content]): The content to compute tokens for.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
response = client.models.compute_tokens(
|
|
model='gemini-2.0-flash',
|
|
contents='What is your name?',
|
|
)
|
|
print(response)
|
|
# tokens_info=[TokensInfo(role='user', token_ids=['1841', ...],
|
|
# tokens=[b'What', b' is', b' your', b' name', b'?'])]
|
|
"""
|
|
|
|
parameter_model = types._ComputeTokensParameters(
|
|
model=model,
|
|
contents=contents,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
if not self._api_client.vertexai:
|
|
raise ValueError('This method is only supported in the Vertex AI client.')
|
|
else:
|
|
request_dict = _ComputeTokensParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:computeTokens'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:computeTokens'
|
|
|
|
query_params = request_dict.get('_query')
|
|
if query_params:
|
|
path = f'{path}?{urlencode(query_params)}'
|
|
# TODO: remove the hack that pops config.
|
|
request_dict.pop('config', None)
|
|
|
|
http_options: Optional[types.HttpOptions] = None
|
|
if (
|
|
parameter_model.config is not None
|
|
and parameter_model.config.http_options is not None
|
|
):
|
|
http_options = parameter_model.config.http_options
|
|
|
|
request_dict = _common.convert_to_dict(request_dict)
|
|
request_dict = _common.encode_unserializable_types(request_dict)
|
|
|
|
response_dict = self._api_client.request(
|
|
'post', path, request_dict, http_options
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _ComputeTokensResponse_from_vertex(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
return_value = types.ComputeTokensResponse._from_response(
|
|
response=response_dict, kwargs=parameter_model.model_dump()
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
def generate_videos(
|
|
self,
|
|
*,
|
|
model: str,
|
|
prompt: Optional[str] = None,
|
|
image: Optional[types.ImageOrDict] = None,
|
|
config: Optional[types.GenerateVideosConfigOrDict] = None,
|
|
) -> types.GenerateVideosOperation:
|
|
"""Generates videos based on a text description and configuration.
|
|
|
|
Args:
|
|
model: The model to use.
|
|
instances: A list of prompts, images and videos to generate videos from.
|
|
config: Configuration for generation.
|
|
|
|
Usage:
|
|
|
|
```
|
|
operation = client.models.generate_videos(
|
|
model="veo-2.0-generate-001",
|
|
prompt="A neon hologram of a cat driving at top speed",
|
|
)
|
|
while not operation.done:
|
|
time.sleep(10)
|
|
operation = client.operations.get(operation)
|
|
|
|
operation.result.generated_videos[0].video.uri
|
|
```
|
|
"""
|
|
|
|
parameter_model = types._GenerateVideosParameters(
|
|
model=model,
|
|
prompt=prompt,
|
|
image=image,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _GenerateVideosParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:predictLongRunning'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:predictLongRunning'
|
|
else:
|
|
request_dict = _GenerateVideosParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:predictLongRunning'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:predictLongRunning'
|
|
query_params = request_dict.get('_query')
|
|
if query_params:
|
|
path = f'{path}?{urlencode(query_params)}'
|
|
# TODO: remove the hack that pops config.
|
|
request_dict.pop('config', None)
|
|
|
|
http_options: Optional[types.HttpOptions] = None
|
|
if (
|
|
parameter_model.config is not None
|
|
and parameter_model.config.http_options is not None
|
|
):
|
|
http_options = parameter_model.config.http_options
|
|
|
|
request_dict = _common.convert_to_dict(request_dict)
|
|
request_dict = _common.encode_unserializable_types(request_dict)
|
|
|
|
response_dict = self._api_client.request(
|
|
'post', path, request_dict, http_options
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _GenerateVideosOperation_from_vertex(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
else:
|
|
response_dict = _GenerateVideosOperation_from_mldev(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
return_value = types.GenerateVideosOperation._from_response(
|
|
response=response_dict, kwargs=parameter_model.model_dump()
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
def generate_content(
|
|
self,
|
|
*,
|
|
model: str,
|
|
contents: Union[types.ContentListUnion, types.ContentListUnionDict],
|
|
config: Optional[types.GenerateContentConfigOrDict] = None,
|
|
) -> types.GenerateContentResponse:
|
|
"""Makes an API request to generate content using a model.
|
|
|
|
For the `model` parameter, supported formats for Vertex AI API include:
|
|
- The Gemini model ID, for example: 'gemini-2.0-flash'
|
|
- The full resource name starts with 'projects/', for example:
|
|
'projects/my-project-id/locations/us-central1/publishers/google/models/gemini-2.0-flash'
|
|
- The partial resource name with 'publishers/', for example:
|
|
'publishers/google/models/gemini-2.0-flash' or
|
|
'publishers/meta/models/llama-3.1-405b-instruct-maas'
|
|
- `/` separated publisher and model name, for example:
|
|
'google/gemini-2.0-flash' or 'meta/llama-3.1-405b-instruct-maas'
|
|
|
|
For the `model` parameter, supported formats for Gemini API include:
|
|
- The Gemini model ID, for example: 'gemini-2.0-flash'
|
|
- The model name starts with 'models/', for example:
|
|
'models/gemini-2.0-flash'
|
|
- For tuned models, the model name starts with 'tunedModels/',
|
|
for example:
|
|
'tunedModels/1234567890123456789'
|
|
|
|
Some models support multimodal input and output.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
from google.genai import types
|
|
from google import genai
|
|
|
|
client = genai.Client(
|
|
vertexai=True, project='my-project-id', location='us-central1'
|
|
)
|
|
|
|
response = client.models.generate_content(
|
|
model='gemini-2.0-flash',
|
|
contents='''What is a good name for a flower shop that specializes in
|
|
selling bouquets of dried flowers?'''
|
|
)
|
|
print(response.text)
|
|
# **Elegant & Classic:**
|
|
# * The Dried Bloom
|
|
# * Everlasting Florals
|
|
# * Timeless Petals
|
|
|
|
response = client.models.generate_content(
|
|
model='gemini-2.0-flash',
|
|
contents=[
|
|
types.Part.from_text('What is shown in this image?'),
|
|
types.Part.from_uri('gs://generativeai-downloads/images/scones.jpg',
|
|
'image/jpeg')
|
|
]
|
|
)
|
|
print(response.text)
|
|
# The image shows a flat lay arrangement of freshly baked blueberry
|
|
# scones.
|
|
"""
|
|
|
|
if _extra_utils.should_disable_afc(config):
|
|
return self._generate_content(
|
|
model=model, contents=contents, config=config
|
|
)
|
|
remaining_remote_calls_afc = _extra_utils.get_max_remote_calls_afc(config)
|
|
logger.info(
|
|
f'AFC is enabled with max remote calls: {remaining_remote_calls_afc}.'
|
|
)
|
|
automatic_function_calling_history: list[types.Content] = []
|
|
response = types.GenerateContentResponse()
|
|
i = 0
|
|
while remaining_remote_calls_afc > 0:
|
|
i += 1
|
|
response = self._generate_content(
|
|
model=model, contents=contents, config=config
|
|
)
|
|
logger.info(f'AFC remote call {i} is done.')
|
|
remaining_remote_calls_afc -= 1
|
|
if remaining_remote_calls_afc == 0:
|
|
logger.info('Reached max remote calls for automatic function calling.')
|
|
|
|
function_map = _extra_utils.get_function_map(config)
|
|
if not function_map:
|
|
break
|
|
if not response:
|
|
break
|
|
if (
|
|
not response.candidates
|
|
or not response.candidates[0].content
|
|
or not response.candidates[0].content.parts
|
|
):
|
|
break
|
|
func_response_parts = _extra_utils.get_function_response_parts(
|
|
response, function_map
|
|
)
|
|
if not func_response_parts:
|
|
break
|
|
func_call_content = response.candidates[0].content
|
|
func_response_content = types.Content(
|
|
role='user',
|
|
parts=func_response_parts,
|
|
)
|
|
contents = t.t_contents(self._api_client, contents) # type: ignore[assignment]
|
|
if not automatic_function_calling_history:
|
|
automatic_function_calling_history.extend(contents) # type: ignore[arg-type]
|
|
if isinstance(contents, list):
|
|
contents.append(func_call_content) # type: ignore[arg-type]
|
|
contents.append(func_response_content) # type: ignore[arg-type]
|
|
automatic_function_calling_history.append(func_call_content)
|
|
automatic_function_calling_history.append(func_response_content)
|
|
if _extra_utils.should_append_afc_history(config) and response is not None:
|
|
response.automatic_function_calling_history = (
|
|
automatic_function_calling_history
|
|
)
|
|
return response
|
|
|
|
def generate_content_stream(
|
|
self,
|
|
*,
|
|
model: str,
|
|
contents: Union[types.ContentListUnion, types.ContentListUnionDict],
|
|
config: Optional[types.GenerateContentConfigOrDict] = None,
|
|
) -> Iterator[types.GenerateContentResponse]:
|
|
"""Makes an API request to generate content using a model and yields the model's response in chunks.
|
|
|
|
For the `model` parameter, supported formats for Vertex AI API include:
|
|
- The Gemini model ID, for example: 'gemini-2.0-flash'
|
|
- The full resource name starts with 'projects/', for example:
|
|
'projects/my-project-id/locations/us-central1/publishers/google/models/gemini-2.0-flash'
|
|
- The partial resource name with 'publishers/', for example:
|
|
'publishers/google/models/gemini-2.0-flash' or
|
|
'publishers/meta/models/llama-3.1-405b-instruct-maas'
|
|
- `/` separated publisher and model name, for example:
|
|
'google/gemini-2.0-flash' or 'meta/llama-3.1-405b-instruct-maas'
|
|
|
|
For the `model` parameter, supported formats for Gemini API include:
|
|
- The Gemini model ID, for example: 'gemini-2.0-flash'
|
|
- The model name starts with 'models/', for example:
|
|
'models/gemini-2.0-flash'
|
|
- For tuned models, the model name starts with 'tunedModels/',
|
|
for example:
|
|
'tunedModels/1234567890123456789'
|
|
|
|
Some models support multimodal input and output.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
from google.genai import types
|
|
from google import genai
|
|
|
|
client = genai.Client(
|
|
vertexai=True, project='my-project-id', location='us-central1'
|
|
)
|
|
|
|
for chunk in client.models.generate_content_stream(
|
|
model='gemini-2.0-flash',
|
|
contents='''What is a good name for a flower shop that specializes in
|
|
selling bouquets of dried flowers?'''
|
|
):
|
|
print(chunk.text)
|
|
# **Elegant & Classic:**
|
|
# * The Dried Bloom
|
|
# * Everlasting Florals
|
|
# * Timeless Petals
|
|
|
|
for chunk in client.models.generate_content_stream(
|
|
model='gemini-2.0-flash',
|
|
contents=[
|
|
types.Part.from_text('What is shown in this image?'),
|
|
types.Part.from_uri('gs://generativeai-downloads/images/scones.jpg',
|
|
'image/jpeg')
|
|
]
|
|
):
|
|
print(chunk.text)
|
|
# The image shows a flat lay arrangement of freshly baked blueberry
|
|
# scones.
|
|
"""
|
|
|
|
if _extra_utils.should_disable_afc(config):
|
|
yield from self._generate_content_stream(
|
|
model=model, contents=contents, config=config
|
|
)
|
|
return
|
|
|
|
remaining_remote_calls_afc = _extra_utils.get_max_remote_calls_afc(config)
|
|
logger.info(
|
|
f'AFC is enabled with max remote calls: {remaining_remote_calls_afc}.'
|
|
)
|
|
automatic_function_calling_history: list[types.Content] = []
|
|
chunk = None
|
|
func_response_parts = None
|
|
i = 0
|
|
while remaining_remote_calls_afc > 0:
|
|
i += 1
|
|
response = self._generate_content_stream(
|
|
model=model, contents=contents, config=config
|
|
)
|
|
logger.info(f'AFC remote call {i} is done.')
|
|
remaining_remote_calls_afc -= 1
|
|
if remaining_remote_calls_afc == 0:
|
|
logger.info('Reached max remote calls for automatic function calling.')
|
|
|
|
function_map = _extra_utils.get_function_map(config)
|
|
|
|
if i == 1:
|
|
# First request gets a function call.
|
|
# Then get function response parts.
|
|
# Yield chunks only if there's no function response parts.
|
|
for chunk in response:
|
|
if not function_map:
|
|
yield chunk
|
|
else:
|
|
if (
|
|
not chunk.candidates
|
|
or not chunk.candidates[0].content
|
|
or not chunk.candidates[0].content.parts
|
|
):
|
|
break
|
|
func_response_parts = _extra_utils.get_function_response_parts(
|
|
chunk, function_map
|
|
)
|
|
if not func_response_parts:
|
|
yield chunk
|
|
|
|
else:
|
|
# Second request and beyond, yield chunks.
|
|
for chunk in response:
|
|
if _extra_utils.should_append_afc_history(config):
|
|
chunk.automatic_function_calling_history = (
|
|
automatic_function_calling_history
|
|
)
|
|
yield chunk
|
|
if (
|
|
not chunk.candidates
|
|
or not chunk.candidates[0].content
|
|
or not chunk.candidates[0].content.parts
|
|
):
|
|
break
|
|
func_response_parts = _extra_utils.get_function_response_parts(
|
|
chunk, function_map
|
|
)
|
|
|
|
if not function_map:
|
|
break
|
|
if not func_response_parts:
|
|
break
|
|
|
|
# Append function response parts to contents for the next request.
|
|
if chunk.candidates is not None:
|
|
func_call_content = chunk.candidates[0].content
|
|
func_response_content = types.Content(
|
|
role='user',
|
|
parts=func_response_parts,
|
|
)
|
|
contents = t.t_contents(self._api_client, contents) # type: ignore[assignment]
|
|
if not automatic_function_calling_history:
|
|
automatic_function_calling_history.extend(contents) # type: ignore[arg-type]
|
|
if isinstance(contents, list) and func_call_content is not None:
|
|
contents.append(func_call_content) # type: ignore[arg-type]
|
|
contents.append(func_response_content) # type: ignore[arg-type]
|
|
if func_call_content is not None:
|
|
automatic_function_calling_history.append(func_call_content)
|
|
automatic_function_calling_history.append(func_response_content)
|
|
|
|
def generate_images(
|
|
self,
|
|
*,
|
|
model: str,
|
|
prompt: str,
|
|
config: Optional[types.GenerateImagesConfigOrDict] = None,
|
|
) -> types.GenerateImagesResponse:
|
|
"""Generates images based on a text description and configuration.
|
|
|
|
Args:
|
|
model (str): The model to use.
|
|
prompt (str): A text description of the images to generate.
|
|
config (GenerateImagesConfig): Configuration for generation.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
response = client.models.generate_images(
|
|
model='imagen-3.0-generate-002',
|
|
prompt='Man with a dog',
|
|
config=types.GenerateImagesConfig(
|
|
number_of_images= 1,
|
|
include_rai_reason= True,
|
|
)
|
|
)
|
|
response.generated_images[0].image.show()
|
|
# Shows a man with a dog.
|
|
"""
|
|
api_response = self._generate_images(
|
|
model=model,
|
|
prompt=prompt,
|
|
config=config,
|
|
)
|
|
positive_prompt_safety_attributes = None
|
|
generated_images = []
|
|
if not api_response or not api_response.generated_images:
|
|
return api_response
|
|
|
|
for generated_image in api_response.generated_images:
|
|
if (
|
|
generated_image.safety_attributes
|
|
and generated_image.safety_attributes.content_type
|
|
== 'Positive Prompt'
|
|
):
|
|
positive_prompt_safety_attributes = generated_image.safety_attributes
|
|
else:
|
|
generated_images.append(generated_image)
|
|
|
|
response = types.GenerateImagesResponse(
|
|
generated_images=generated_images,
|
|
positive_prompt_safety_attributes=positive_prompt_safety_attributes,
|
|
)
|
|
return response
|
|
|
|
def edit_image(
|
|
self,
|
|
*,
|
|
model: str,
|
|
prompt: str,
|
|
reference_images: list[types._ReferenceImageAPIOrDict],
|
|
config: Optional[types.EditImageConfigOrDict] = None,
|
|
) -> types.EditImageResponse:
|
|
"""Edits an image based on a text description and configuration.
|
|
|
|
Args:
|
|
model (str): The model to use.
|
|
prompt (str): A text description of the edit to apply to the image.
|
|
reference_images (list[Union[RawReferenceImage, MaskReferenceImage,
|
|
ControlReferenceImage, StyleReferenceImage, SubjectReferenceImage]): The
|
|
reference images for editing.
|
|
config (EditImageConfig): Configuration for editing.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
from google.genai.types import RawReferenceImage, MaskReferenceImage
|
|
|
|
raw_ref_image = RawReferenceImage(
|
|
reference_id=1,
|
|
reference_image=types.Image.from_file(IMAGE_FILE_PATH),
|
|
)
|
|
|
|
mask_ref_image = MaskReferenceImage(
|
|
reference_id=2,
|
|
config=types.MaskReferenceConfig(
|
|
mask_mode='MASK_MODE_FOREGROUND',
|
|
mask_dilation=0.06,
|
|
),
|
|
)
|
|
response = client.models.edit_image(
|
|
model='imagen-3.0-capability-001',
|
|
prompt='man with dog',
|
|
reference_images=[raw_ref_image, mask_ref_image],
|
|
config=types.EditImageConfig(
|
|
edit_mode= "EDIT_MODE_INPAINT_INSERTION",
|
|
number_of_images= 1,
|
|
include_rai_reason= True,
|
|
)
|
|
)
|
|
response.generated_images[0].image.show()
|
|
# Shows a man with a dog instead of a cat.
|
|
"""
|
|
return self._edit_image(
|
|
model=model,
|
|
prompt=prompt,
|
|
reference_images=reference_images,
|
|
config=config,
|
|
)
|
|
|
|
def upscale_image(
|
|
self,
|
|
*,
|
|
model: str,
|
|
image: types.ImageOrDict,
|
|
upscale_factor: str,
|
|
config: Optional[types.UpscaleImageConfigOrDict] = None,
|
|
) -> types.UpscaleImageResponse:
|
|
"""Makes an API request to upscale a provided image.
|
|
|
|
Args:
|
|
model (str): The model to use.
|
|
image (Image): The input image for upscaling.
|
|
upscale_factor (str): The factor to upscale the image (x2 or x4).
|
|
config (UpscaleImageConfig): Configuration for upscaling.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
from google.genai.types import Image
|
|
|
|
IMAGE_FILE_PATH="my-image.png"
|
|
response=client.models.upscale_image(
|
|
model='imagen-3.0-generate-001',
|
|
image=types.Image.from_file(IMAGE_FILE_PATH),
|
|
upscale_factor='x2',
|
|
)
|
|
response.generated_images[0].image.show()
|
|
# Opens my-image.png which is upscaled by a factor of 2.
|
|
"""
|
|
|
|
# Validate config.
|
|
types.UpscaleImageParameters(
|
|
model=model,
|
|
image=image,
|
|
upscale_factor=upscale_factor,
|
|
config=config,
|
|
)
|
|
|
|
# Convert to API config.
|
|
config = config or {}
|
|
if isinstance(config, types.UpscaleImageConfig):
|
|
config_dct = config.model_dump()
|
|
else:
|
|
config_dct = dict(config)
|
|
api_config = types._UpscaleImageAPIConfigDict(
|
|
http_options=config_dct.get('http_options', None),
|
|
include_rai_reason=config_dct.get('include_rai_reason', None),
|
|
output_mime_type=config_dct.get('output_mime_type', None),
|
|
output_compression_quality=config_dct.get(
|
|
'output_compression_quality', None
|
|
),
|
|
) # pylint: disable=protected-access
|
|
|
|
# Provide default values through API config.
|
|
api_config['mode'] = 'upscale'
|
|
api_config['number_of_images'] = 1
|
|
|
|
return self._upscale_image(
|
|
model=model,
|
|
image=image,
|
|
upscale_factor=upscale_factor,
|
|
config=api_config,
|
|
)
|
|
|
|
def list(
|
|
self,
|
|
*,
|
|
config: Optional[types.ListModelsConfigOrDict] = None,
|
|
) -> Pager[types.Model]:
|
|
"""Makes an API request to list the available models.
|
|
|
|
If `query_base` is set to True in the config or not set (default), the
|
|
API will return all available base models. If set to False, it will return
|
|
all tuned models.
|
|
|
|
Args:
|
|
config (ListModelsConfigOrDict): Configuration for retrieving models.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
response=client.models.list(config={'page_size': 5})
|
|
print(response.page)
|
|
# [Model(name='projects/./locations/./models/123', display_name='my_model'
|
|
|
|
response=client.models.list(config={'page_size': 5, 'query_base': True})
|
|
print(response.page)
|
|
# [Model(name='publishers/google/models/gemini-2.0-flash-exp' ...
|
|
"""
|
|
|
|
config = (
|
|
types._ListModelsParameters(config=config).config
|
|
or types.ListModelsConfig()
|
|
)
|
|
if config.query_base is None:
|
|
config.query_base = True
|
|
if self._api_client.vertexai:
|
|
config = config.copy()
|
|
if not config.query_base:
|
|
# Filter for tuning jobs artifacts by labels.
|
|
filter_value = config.filter
|
|
config.filter = (
|
|
filter_value + '&filter=labels.tune-type:*'
|
|
if filter_value
|
|
else 'labels.tune-type:*'
|
|
)
|
|
return Pager(
|
|
'models',
|
|
self._list,
|
|
self._list(config=config),
|
|
config,
|
|
)
|
|
|
|
|
|
class AsyncModels(_api_module.BaseModule):
|
|
|
|
async def _generate_content(
|
|
self,
|
|
*,
|
|
model: str,
|
|
contents: Union[types.ContentListUnion, types.ContentListUnionDict],
|
|
config: Optional[types.GenerateContentConfigOrDict] = None,
|
|
) -> types.GenerateContentResponse:
|
|
parameter_model = types._GenerateContentParameters(
|
|
model=model,
|
|
contents=contents,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _GenerateContentParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:generateContent'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:generateContent'
|
|
else:
|
|
request_dict = _GenerateContentParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:generateContent'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:generateContent'
|
|
query_params = request_dict.get('_query')
|
|
if query_params:
|
|
path = f'{path}?{urlencode(query_params)}'
|
|
# TODO: remove the hack that pops config.
|
|
request_dict.pop('config', None)
|
|
|
|
http_options: Optional[types.HttpOptions] = None
|
|
if (
|
|
parameter_model.config is not None
|
|
and parameter_model.config.http_options is not None
|
|
):
|
|
http_options = parameter_model.config.http_options
|
|
|
|
request_dict = _common.convert_to_dict(request_dict)
|
|
request_dict = _common.encode_unserializable_types(request_dict)
|
|
|
|
response_dict = await self._api_client.async_request(
|
|
'post', path, request_dict, http_options
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _GenerateContentResponse_from_vertex(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
else:
|
|
response_dict = _GenerateContentResponse_from_mldev(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
return_value = types.GenerateContentResponse._from_response(
|
|
response=response_dict, kwargs=parameter_model.model_dump()
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
async def _generate_content_stream(
|
|
self,
|
|
*,
|
|
model: str,
|
|
contents: Union[types.ContentListUnion, types.ContentListUnionDict],
|
|
config: Optional[types.GenerateContentConfigOrDict] = None,
|
|
) -> Awaitable[AsyncIterator[types.GenerateContentResponse]]:
|
|
parameter_model = types._GenerateContentParameters(
|
|
model=model,
|
|
contents=contents,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _GenerateContentParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:streamGenerateContent?alt=sse'.format_map(
|
|
request_url_dict
|
|
)
|
|
else:
|
|
path = '{model}:streamGenerateContent?alt=sse'
|
|
else:
|
|
request_dict = _GenerateContentParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:streamGenerateContent?alt=sse'.format_map(
|
|
request_url_dict
|
|
)
|
|
else:
|
|
path = '{model}:streamGenerateContent?alt=sse'
|
|
query_params = request_dict.get('_query')
|
|
if query_params:
|
|
path = f'{path}?{urlencode(query_params)}'
|
|
# TODO: remove the hack that pops config.
|
|
request_dict.pop('config', None)
|
|
|
|
http_options: Optional[types.HttpOptions] = None
|
|
if (
|
|
parameter_model.config is not None
|
|
and parameter_model.config.http_options is not None
|
|
):
|
|
http_options = parameter_model.config.http_options
|
|
|
|
request_dict = _common.convert_to_dict(request_dict)
|
|
request_dict = _common.encode_unserializable_types(request_dict)
|
|
|
|
response_stream = await self._api_client.async_request_streamed(
|
|
'post', path, request_dict, http_options
|
|
)
|
|
|
|
async def async_generator(): # type: ignore[no-untyped-def]
|
|
async for response_dict in response_stream:
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _GenerateContentResponse_from_vertex(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
else:
|
|
response_dict = _GenerateContentResponse_from_mldev(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
return_value = types.GenerateContentResponse._from_response(
|
|
response=response_dict, kwargs=parameter_model.model_dump()
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
yield return_value
|
|
|
|
return async_generator() # type: ignore[no-untyped-call, no-any-return]
|
|
|
|
async def embed_content(
|
|
self,
|
|
*,
|
|
model: str,
|
|
contents: Union[types.ContentListUnion, types.ContentListUnionDict],
|
|
config: Optional[types.EmbedContentConfigOrDict] = None,
|
|
) -> types.EmbedContentResponse:
|
|
"""Calculates embeddings for the given contents. Only text is supported.
|
|
|
|
Args:
|
|
model (str): The model to use.
|
|
contents (list[Content]): The contents to embed.
|
|
config (EmbedContentConfig): Optional configuration for embeddings.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
embeddings = await client.aio.models.embed_content(
|
|
model= 'text-embedding-004',
|
|
contents=[
|
|
'What is your name?',
|
|
'What is your favorite color?',
|
|
],
|
|
config={
|
|
'output_dimensionality': 64
|
|
},
|
|
)
|
|
"""
|
|
|
|
parameter_model = types._EmbedContentParameters(
|
|
model=model,
|
|
contents=contents,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _EmbedContentParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:predict'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:predict'
|
|
else:
|
|
request_dict = _EmbedContentParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:batchEmbedContents'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:batchEmbedContents'
|
|
query_params = request_dict.get('_query')
|
|
if query_params:
|
|
path = f'{path}?{urlencode(query_params)}'
|
|
# TODO: remove the hack that pops config.
|
|
request_dict.pop('config', None)
|
|
|
|
http_options: Optional[types.HttpOptions] = None
|
|
if (
|
|
parameter_model.config is not None
|
|
and parameter_model.config.http_options is not None
|
|
):
|
|
http_options = parameter_model.config.http_options
|
|
|
|
request_dict = _common.convert_to_dict(request_dict)
|
|
request_dict = _common.encode_unserializable_types(request_dict)
|
|
|
|
response_dict = await self._api_client.async_request(
|
|
'post', path, request_dict, http_options
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _EmbedContentResponse_from_vertex(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
else:
|
|
response_dict = _EmbedContentResponse_from_mldev(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
return_value = types.EmbedContentResponse._from_response(
|
|
response=response_dict, kwargs=parameter_model.model_dump()
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
async def _generate_images(
|
|
self,
|
|
*,
|
|
model: str,
|
|
prompt: str,
|
|
config: Optional[types.GenerateImagesConfigOrDict] = None,
|
|
) -> types.GenerateImagesResponse:
|
|
"""Generates images based on a text description and configuration.
|
|
|
|
Args:
|
|
model (str): The model to use.
|
|
prompt (str): A text description of the images to generate.
|
|
config (GenerateImagesConfig): Configuration for generation.
|
|
"""
|
|
|
|
parameter_model = types._GenerateImagesParameters(
|
|
model=model,
|
|
prompt=prompt,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _GenerateImagesParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:predict'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:predict'
|
|
else:
|
|
request_dict = _GenerateImagesParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:predict'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:predict'
|
|
query_params = request_dict.get('_query')
|
|
if query_params:
|
|
path = f'{path}?{urlencode(query_params)}'
|
|
# TODO: remove the hack that pops config.
|
|
request_dict.pop('config', None)
|
|
|
|
http_options: Optional[types.HttpOptions] = None
|
|
if (
|
|
parameter_model.config is not None
|
|
and parameter_model.config.http_options is not None
|
|
):
|
|
http_options = parameter_model.config.http_options
|
|
|
|
request_dict = _common.convert_to_dict(request_dict)
|
|
request_dict = _common.encode_unserializable_types(request_dict)
|
|
|
|
response_dict = await self._api_client.async_request(
|
|
'post', path, request_dict, http_options
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _GenerateImagesResponse_from_vertex(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
else:
|
|
response_dict = _GenerateImagesResponse_from_mldev(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
return_value = types.GenerateImagesResponse._from_response(
|
|
response=response_dict, kwargs=parameter_model.model_dump()
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
async def _edit_image(
|
|
self,
|
|
*,
|
|
model: str,
|
|
prompt: str,
|
|
reference_images: list[types._ReferenceImageAPIOrDict],
|
|
config: Optional[types.EditImageConfigOrDict] = None,
|
|
) -> types.EditImageResponse:
|
|
"""Edits an image based on a text description and configuration.
|
|
|
|
Args:
|
|
model (str): The model to use.
|
|
prompt (str): A text description of the edit to apply to the image.
|
|
reference_images (list[Union[RawReferenceImage, MaskReferenceImage,
|
|
ControlReferenceImage, StyleReferenceImage, SubjectReferenceImage]): The
|
|
reference images for editing.
|
|
config (EditImageConfig): Configuration for editing.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
from google.genai.types import RawReferenceImage, MaskReferenceImage
|
|
|
|
raw_ref_image = RawReferenceImage(
|
|
reference_id=1,
|
|
reference_image=types.Image.from_file(IMAGE_FILE_PATH),
|
|
)
|
|
|
|
mask_ref_image = MaskReferenceImage(
|
|
reference_id=2,
|
|
config=types.MaskReferenceConfig(
|
|
mask_mode='MASK_MODE_FOREGROUND',
|
|
mask_dilation=0.06,
|
|
),
|
|
)
|
|
response = await client.aio.models.edit_image(
|
|
model='imagen-3.0-capability-001',
|
|
prompt='man with dog',
|
|
reference_images=[raw_ref_image, mask_ref_image],
|
|
config=types.EditImageConfig(
|
|
edit_mode= "EDIT_MODE_INPAINT_INSERTION",
|
|
number_of_images= 1,
|
|
include_rai_reason= True,
|
|
)
|
|
)
|
|
response.generated_images[0].image.show()
|
|
# Shows a man with a dog instead of a cat.
|
|
"""
|
|
|
|
parameter_model = types._EditImageParameters(
|
|
model=model,
|
|
prompt=prompt,
|
|
reference_images=reference_images,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
if not self._api_client.vertexai:
|
|
raise ValueError('This method is only supported in the Vertex AI client.')
|
|
else:
|
|
request_dict = _EditImageParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:predict'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:predict'
|
|
|
|
query_params = request_dict.get('_query')
|
|
if query_params:
|
|
path = f'{path}?{urlencode(query_params)}'
|
|
# TODO: remove the hack that pops config.
|
|
request_dict.pop('config', None)
|
|
|
|
http_options: Optional[types.HttpOptions] = None
|
|
if (
|
|
parameter_model.config is not None
|
|
and parameter_model.config.http_options is not None
|
|
):
|
|
http_options = parameter_model.config.http_options
|
|
|
|
request_dict = _common.convert_to_dict(request_dict)
|
|
request_dict = _common.encode_unserializable_types(request_dict)
|
|
|
|
response_dict = await self._api_client.async_request(
|
|
'post', path, request_dict, http_options
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _EditImageResponse_from_vertex(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
return_value = types.EditImageResponse._from_response(
|
|
response=response_dict, kwargs=parameter_model.model_dump()
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
async def _upscale_image(
|
|
self,
|
|
*,
|
|
model: str,
|
|
image: types.ImageOrDict,
|
|
upscale_factor: str,
|
|
config: Optional[types._UpscaleImageAPIConfigOrDict] = None,
|
|
) -> types.UpscaleImageResponse:
|
|
"""Upscales an image.
|
|
|
|
Args:
|
|
model (str): The model to use.
|
|
image (Image): The input image for upscaling.
|
|
upscale_factor (str): The factor to upscale the image (x2 or x4).
|
|
config (_UpscaleImageAPIConfig): Configuration for upscaling.
|
|
"""
|
|
|
|
parameter_model = types._UpscaleImageAPIParameters(
|
|
model=model,
|
|
image=image,
|
|
upscale_factor=upscale_factor,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
if not self._api_client.vertexai:
|
|
raise ValueError('This method is only supported in the Vertex AI client.')
|
|
else:
|
|
request_dict = _UpscaleImageAPIParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:predict'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:predict'
|
|
|
|
query_params = request_dict.get('_query')
|
|
if query_params:
|
|
path = f'{path}?{urlencode(query_params)}'
|
|
# TODO: remove the hack that pops config.
|
|
request_dict.pop('config', None)
|
|
|
|
http_options: Optional[types.HttpOptions] = None
|
|
if (
|
|
parameter_model.config is not None
|
|
and parameter_model.config.http_options is not None
|
|
):
|
|
http_options = parameter_model.config.http_options
|
|
|
|
request_dict = _common.convert_to_dict(request_dict)
|
|
request_dict = _common.encode_unserializable_types(request_dict)
|
|
|
|
response_dict = await self._api_client.async_request(
|
|
'post', path, request_dict, http_options
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _UpscaleImageResponse_from_vertex(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
return_value = types.UpscaleImageResponse._from_response(
|
|
response=response_dict, kwargs=parameter_model.model_dump()
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
async def get(
|
|
self, *, model: str, config: Optional[types.GetModelConfigOrDict] = None
|
|
) -> types.Model:
|
|
parameter_model = types._GetModelParameters(
|
|
model=model,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _GetModelParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{name}'.format_map(request_url_dict)
|
|
else:
|
|
path = '{name}'
|
|
else:
|
|
request_dict = _GetModelParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{name}'.format_map(request_url_dict)
|
|
else:
|
|
path = '{name}'
|
|
query_params = request_dict.get('_query')
|
|
if query_params:
|
|
path = f'{path}?{urlencode(query_params)}'
|
|
# TODO: remove the hack that pops config.
|
|
request_dict.pop('config', None)
|
|
|
|
http_options: Optional[types.HttpOptions] = None
|
|
if (
|
|
parameter_model.config is not None
|
|
and parameter_model.config.http_options is not None
|
|
):
|
|
http_options = parameter_model.config.http_options
|
|
|
|
request_dict = _common.convert_to_dict(request_dict)
|
|
request_dict = _common.encode_unserializable_types(request_dict)
|
|
|
|
response_dict = await self._api_client.async_request(
|
|
'get', path, request_dict, http_options
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _Model_from_vertex(self._api_client, response_dict)
|
|
|
|
else:
|
|
response_dict = _Model_from_mldev(self._api_client, response_dict)
|
|
|
|
return_value = types.Model._from_response(
|
|
response=response_dict, kwargs=parameter_model.model_dump()
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
async def _list(
|
|
self, *, config: Optional[types.ListModelsConfigOrDict] = None
|
|
) -> types.ListModelsResponse:
|
|
parameter_model = types._ListModelsParameters(
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _ListModelsParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{models_url}'.format_map(request_url_dict)
|
|
else:
|
|
path = '{models_url}'
|
|
else:
|
|
request_dict = _ListModelsParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{models_url}'.format_map(request_url_dict)
|
|
else:
|
|
path = '{models_url}'
|
|
query_params = request_dict.get('_query')
|
|
if query_params:
|
|
path = f'{path}?{urlencode(query_params)}'
|
|
# TODO: remove the hack that pops config.
|
|
request_dict.pop('config', None)
|
|
|
|
http_options: Optional[types.HttpOptions] = None
|
|
if (
|
|
parameter_model.config is not None
|
|
and parameter_model.config.http_options is not None
|
|
):
|
|
http_options = parameter_model.config.http_options
|
|
|
|
request_dict = _common.convert_to_dict(request_dict)
|
|
request_dict = _common.encode_unserializable_types(request_dict)
|
|
|
|
response_dict = await self._api_client.async_request(
|
|
'get', path, request_dict, http_options
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _ListModelsResponse_from_vertex(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
else:
|
|
response_dict = _ListModelsResponse_from_mldev(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
return_value = types.ListModelsResponse._from_response(
|
|
response=response_dict, kwargs=parameter_model.model_dump()
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
async def update(
|
|
self,
|
|
*,
|
|
model: str,
|
|
config: Optional[types.UpdateModelConfigOrDict] = None,
|
|
) -> types.Model:
|
|
parameter_model = types._UpdateModelParameters(
|
|
model=model,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _UpdateModelParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}'
|
|
else:
|
|
request_dict = _UpdateModelParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{name}'.format_map(request_url_dict)
|
|
else:
|
|
path = '{name}'
|
|
query_params = request_dict.get('_query')
|
|
if query_params:
|
|
path = f'{path}?{urlencode(query_params)}'
|
|
# TODO: remove the hack that pops config.
|
|
request_dict.pop('config', None)
|
|
|
|
http_options: Optional[types.HttpOptions] = None
|
|
if (
|
|
parameter_model.config is not None
|
|
and parameter_model.config.http_options is not None
|
|
):
|
|
http_options = parameter_model.config.http_options
|
|
|
|
request_dict = _common.convert_to_dict(request_dict)
|
|
request_dict = _common.encode_unserializable_types(request_dict)
|
|
|
|
response_dict = await self._api_client.async_request(
|
|
'patch', path, request_dict, http_options
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _Model_from_vertex(self._api_client, response_dict)
|
|
|
|
else:
|
|
response_dict = _Model_from_mldev(self._api_client, response_dict)
|
|
|
|
return_value = types.Model._from_response(
|
|
response=response_dict, kwargs=parameter_model.model_dump()
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
async def delete(
|
|
self,
|
|
*,
|
|
model: str,
|
|
config: Optional[types.DeleteModelConfigOrDict] = None,
|
|
) -> types.DeleteModelResponse:
|
|
parameter_model = types._DeleteModelParameters(
|
|
model=model,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _DeleteModelParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{name}'.format_map(request_url_dict)
|
|
else:
|
|
path = '{name}'
|
|
else:
|
|
request_dict = _DeleteModelParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{name}'.format_map(request_url_dict)
|
|
else:
|
|
path = '{name}'
|
|
query_params = request_dict.get('_query')
|
|
if query_params:
|
|
path = f'{path}?{urlencode(query_params)}'
|
|
# TODO: remove the hack that pops config.
|
|
request_dict.pop('config', None)
|
|
|
|
http_options: Optional[types.HttpOptions] = None
|
|
if (
|
|
parameter_model.config is not None
|
|
and parameter_model.config.http_options is not None
|
|
):
|
|
http_options = parameter_model.config.http_options
|
|
|
|
request_dict = _common.convert_to_dict(request_dict)
|
|
request_dict = _common.encode_unserializable_types(request_dict)
|
|
|
|
response_dict = await self._api_client.async_request(
|
|
'delete', path, request_dict, http_options
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _DeleteModelResponse_from_vertex(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
else:
|
|
response_dict = _DeleteModelResponse_from_mldev(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
return_value = types.DeleteModelResponse._from_response(
|
|
response=response_dict, kwargs=parameter_model.model_dump()
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
async def count_tokens(
|
|
self,
|
|
*,
|
|
model: str,
|
|
contents: Union[types.ContentListUnion, types.ContentListUnionDict],
|
|
config: Optional[types.CountTokensConfigOrDict] = None,
|
|
) -> types.CountTokensResponse:
|
|
"""Counts the number of tokens in the given content.
|
|
|
|
Multimodal input is supported for Gemini models.
|
|
|
|
Args:
|
|
model (str): The model to use for counting tokens.
|
|
contents (list[types.Content]): The content to count tokens for.
|
|
config (CountTokensConfig): The configuration for counting tokens.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
response = await client.aio.models.count_tokens(
|
|
model='gemini-2.0-flash',
|
|
contents='What is your name?',
|
|
)
|
|
print(response)
|
|
# total_tokens=5 cached_content_token_count=None
|
|
"""
|
|
|
|
parameter_model = types._CountTokensParameters(
|
|
model=model,
|
|
contents=contents,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _CountTokensParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:countTokens'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:countTokens'
|
|
else:
|
|
request_dict = _CountTokensParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:countTokens'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:countTokens'
|
|
query_params = request_dict.get('_query')
|
|
if query_params:
|
|
path = f'{path}?{urlencode(query_params)}'
|
|
# TODO: remove the hack that pops config.
|
|
request_dict.pop('config', None)
|
|
|
|
http_options: Optional[types.HttpOptions] = None
|
|
if (
|
|
parameter_model.config is not None
|
|
and parameter_model.config.http_options is not None
|
|
):
|
|
http_options = parameter_model.config.http_options
|
|
|
|
request_dict = _common.convert_to_dict(request_dict)
|
|
request_dict = _common.encode_unserializable_types(request_dict)
|
|
|
|
response_dict = await self._api_client.async_request(
|
|
'post', path, request_dict, http_options
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _CountTokensResponse_from_vertex(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
else:
|
|
response_dict = _CountTokensResponse_from_mldev(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
return_value = types.CountTokensResponse._from_response(
|
|
response=response_dict, kwargs=parameter_model.model_dump()
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
async def compute_tokens(
|
|
self,
|
|
*,
|
|
model: str,
|
|
contents: Union[types.ContentListUnion, types.ContentListUnionDict],
|
|
config: Optional[types.ComputeTokensConfigOrDict] = None,
|
|
) -> types.ComputeTokensResponse:
|
|
"""Given a list of contents, returns a corresponding TokensInfo containing the
|
|
|
|
list of tokens and list of token ids.
|
|
|
|
|
|
Args:
|
|
model (str): The model to use.
|
|
contents (list[shared.Content]): The content to compute tokens for.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
response = await client.aio.models.compute_tokens(
|
|
model='gemini-2.0-flash',
|
|
contents='What is your name?',
|
|
)
|
|
print(response)
|
|
# tokens_info=[TokensInfo(role='user', token_ids=['1841', ...],
|
|
# tokens=[b'What', b' is', b' your', b' name', b'?'])]
|
|
"""
|
|
|
|
parameter_model = types._ComputeTokensParameters(
|
|
model=model,
|
|
contents=contents,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
if not self._api_client.vertexai:
|
|
raise ValueError('This method is only supported in the Vertex AI client.')
|
|
else:
|
|
request_dict = _ComputeTokensParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:computeTokens'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:computeTokens'
|
|
|
|
query_params = request_dict.get('_query')
|
|
if query_params:
|
|
path = f'{path}?{urlencode(query_params)}'
|
|
# TODO: remove the hack that pops config.
|
|
request_dict.pop('config', None)
|
|
|
|
http_options: Optional[types.HttpOptions] = None
|
|
if (
|
|
parameter_model.config is not None
|
|
and parameter_model.config.http_options is not None
|
|
):
|
|
http_options = parameter_model.config.http_options
|
|
|
|
request_dict = _common.convert_to_dict(request_dict)
|
|
request_dict = _common.encode_unserializable_types(request_dict)
|
|
|
|
response_dict = await self._api_client.async_request(
|
|
'post', path, request_dict, http_options
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _ComputeTokensResponse_from_vertex(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
return_value = types.ComputeTokensResponse._from_response(
|
|
response=response_dict, kwargs=parameter_model.model_dump()
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
async def generate_videos(
|
|
self,
|
|
*,
|
|
model: str,
|
|
prompt: Optional[str] = None,
|
|
image: Optional[types.ImageOrDict] = None,
|
|
config: Optional[types.GenerateVideosConfigOrDict] = None,
|
|
) -> types.GenerateVideosOperation:
|
|
"""Generates videos based on a text description and configuration.
|
|
|
|
Args:
|
|
model: The model to use.
|
|
instances: A list of prompts, images and videos to generate videos from.
|
|
config: Configuration for generation.
|
|
|
|
Usage:
|
|
|
|
```
|
|
operation = client.models.generate_videos(
|
|
model="veo-2.0-generate-001",
|
|
prompt="A neon hologram of a cat driving at top speed",
|
|
)
|
|
while not operation.done:
|
|
time.sleep(10)
|
|
operation = client.operations.get(operation)
|
|
|
|
operation.result.generated_videos[0].video.uri
|
|
```
|
|
"""
|
|
|
|
parameter_model = types._GenerateVideosParameters(
|
|
model=model,
|
|
prompt=prompt,
|
|
image=image,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _GenerateVideosParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:predictLongRunning'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:predictLongRunning'
|
|
else:
|
|
request_dict = _GenerateVideosParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:predictLongRunning'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:predictLongRunning'
|
|
query_params = request_dict.get('_query')
|
|
if query_params:
|
|
path = f'{path}?{urlencode(query_params)}'
|
|
# TODO: remove the hack that pops config.
|
|
request_dict.pop('config', None)
|
|
|
|
http_options: Optional[types.HttpOptions] = None
|
|
if (
|
|
parameter_model.config is not None
|
|
and parameter_model.config.http_options is not None
|
|
):
|
|
http_options = parameter_model.config.http_options
|
|
|
|
request_dict = _common.convert_to_dict(request_dict)
|
|
request_dict = _common.encode_unserializable_types(request_dict)
|
|
|
|
response_dict = await self._api_client.async_request(
|
|
'post', path, request_dict, http_options
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _GenerateVideosOperation_from_vertex(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
else:
|
|
response_dict = _GenerateVideosOperation_from_mldev(
|
|
self._api_client, response_dict
|
|
)
|
|
|
|
return_value = types.GenerateVideosOperation._from_response(
|
|
response=response_dict, kwargs=parameter_model.model_dump()
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
async def generate_content(
|
|
self,
|
|
*,
|
|
model: str,
|
|
contents: Union[types.ContentListUnion, types.ContentListUnionDict],
|
|
config: Optional[types.GenerateContentConfigOrDict] = None,
|
|
) -> types.GenerateContentResponse:
|
|
"""Makes an API request to generate content using a model.
|
|
|
|
Some models support multimodal input and output.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
from google.genai import types
|
|
from google import genai
|
|
|
|
client = genai.Client(
|
|
vertexai=True, project='my-project-id', location='us-central1'
|
|
)
|
|
|
|
response = await client.aio.models.generate_content(
|
|
model='gemini-2.0-flash',
|
|
contents='User input: I like bagels. Answer:',
|
|
config=types.GenerateContentConfig(
|
|
system_instruction=
|
|
[
|
|
'You are a helpful language translator.',
|
|
'Your mission is to translate text in English to French.'
|
|
]
|
|
),
|
|
)
|
|
print(response.text)
|
|
# J'aime les bagels.
|
|
"""
|
|
if _extra_utils.should_disable_afc(config):
|
|
return await self._generate_content(
|
|
model=model, contents=contents, config=config
|
|
)
|
|
remaining_remote_calls_afc = _extra_utils.get_max_remote_calls_afc(config)
|
|
logger.info(
|
|
f'AFC is enabled with max remote calls: {remaining_remote_calls_afc}.'
|
|
)
|
|
automatic_function_calling_history: list[types.Content] = []
|
|
response = types.GenerateContentResponse()
|
|
while remaining_remote_calls_afc > 0:
|
|
response = await self._generate_content(
|
|
model=model, contents=contents, config=config
|
|
)
|
|
remaining_remote_calls_afc -= 1
|
|
if remaining_remote_calls_afc == 0:
|
|
logger.info('Reached max remote calls for automatic function calling.')
|
|
|
|
function_map = _extra_utils.get_function_map(
|
|
config, is_caller_method_async=True
|
|
)
|
|
if not function_map:
|
|
break
|
|
if not response:
|
|
break
|
|
if (
|
|
not response.candidates
|
|
or not response.candidates[0].content
|
|
or not response.candidates[0].content.parts
|
|
):
|
|
break
|
|
func_response_parts = (
|
|
await _extra_utils.get_function_response_parts_async(
|
|
response, function_map
|
|
)
|
|
)
|
|
if not func_response_parts:
|
|
break
|
|
func_call_content = response.candidates[0].content
|
|
func_response_content = types.Content(
|
|
role='user',
|
|
parts=func_response_parts,
|
|
)
|
|
contents = t.t_contents(self._api_client, contents) # type: ignore[assignment]
|
|
if not automatic_function_calling_history:
|
|
automatic_function_calling_history.extend(contents) # type: ignore[arg-type]
|
|
if isinstance(contents, list):
|
|
contents.append(func_call_content) # type: ignore[arg-type]
|
|
contents.append(func_response_content) # type: ignore[arg-type]
|
|
automatic_function_calling_history.append(func_call_content)
|
|
automatic_function_calling_history.append(func_response_content)
|
|
|
|
if _extra_utils.should_append_afc_history(config) and response is not None:
|
|
response.automatic_function_calling_history = (
|
|
automatic_function_calling_history
|
|
)
|
|
return response
|
|
|
|
async def generate_content_stream(
|
|
self,
|
|
*,
|
|
model: str,
|
|
contents: Union[types.ContentListUnion, types.ContentListUnionDict],
|
|
config: Optional[types.GenerateContentConfigOrDict] = None,
|
|
) -> AsyncIterator[types.GenerateContentResponse]:
|
|
"""Makes an API request to generate content using a model and yields the model's response in chunks.
|
|
|
|
For the `model` parameter, supported formats for Vertex AI API include:
|
|
- The Gemini model ID, for example: 'gemini-2.0-flash'
|
|
- The full resource name starts with 'projects/', for example:
|
|
'projects/my-project-id/locations/us-central1/publishers/google/models/gemini-2.0-flash'
|
|
- The partial resource name with 'publishers/', for example:
|
|
'publishers/google/models/gemini-2.0-flash' or
|
|
'publishers/meta/models/llama-3.1-405b-instruct-maas'
|
|
- `/` separated publisher and model name, for example:
|
|
'google/gemini-2.0-flash' or 'meta/llama-3.1-405b-instruct-maas'
|
|
|
|
For the `model` parameter, supported formats for Gemini API include:
|
|
- The Gemini model ID, for example: 'gemini-2.0-flash'
|
|
- The model name starts with 'models/', for example:
|
|
'models/gemini-2.0-flash'
|
|
- For tuned models, the model name starts with 'tunedModels/',
|
|
for example:
|
|
'tunedModels/1234567890123456789'
|
|
|
|
Some models support multimodal input and output.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
from google.genai import types
|
|
from google import genai
|
|
|
|
client = genai.Client(
|
|
vertexai=True, project='my-project-id', location='us-central1'
|
|
)
|
|
|
|
async for chunk in await client.aio.models.generate_content_stream(
|
|
model='gemini-2.0-flash',
|
|
contents='''What is a good name for a flower shop that specializes in
|
|
selling bouquets of dried flowers?'''
|
|
):
|
|
print(chunk.text)
|
|
# **Elegant & Classic:**
|
|
# * The Dried Bloom
|
|
# * Everlasting Florals
|
|
# * Timeless Petals
|
|
|
|
async for chunk in awiat client.aio.models.generate_content_stream(
|
|
model='gemini-2.0-flash',
|
|
contents=[
|
|
types.Part.from_text('What is shown in this image?'),
|
|
types.Part.from_uri('gs://generativeai-downloads/images/scones.jpg',
|
|
'image/jpeg')
|
|
]
|
|
):
|
|
print(chunk.text)
|
|
# The image shows a flat lay arrangement of freshly baked blueberry
|
|
# scones.
|
|
"""
|
|
|
|
if _extra_utils.should_disable_afc(config):
|
|
response = await self._generate_content_stream(
|
|
model=model, contents=contents, config=config
|
|
)
|
|
|
|
async def base_async_generator(model, contents, config): # type: ignore[no-untyped-def]
|
|
async for chunk in response: # type: ignore[attr-defined]
|
|
yield chunk
|
|
|
|
return base_async_generator(model, contents, config) # type: ignore[no-untyped-call, no-any-return]
|
|
|
|
async def async_generator(model, contents, config): # type: ignore[no-untyped-def]
|
|
remaining_remote_calls_afc = _extra_utils.get_max_remote_calls_afc(config)
|
|
logger.info(
|
|
f'AFC is enabled with max remote calls: {remaining_remote_calls_afc}.'
|
|
)
|
|
automatic_function_calling_history: list[types.Content] = []
|
|
func_response_parts = None
|
|
chunk = None
|
|
i = 0
|
|
while remaining_remote_calls_afc > 0:
|
|
i += 1
|
|
response = await self._generate_content_stream(
|
|
model=model, contents=contents, config=config
|
|
)
|
|
logger.info(f'AFC remote call {i} is done.')
|
|
remaining_remote_calls_afc -= 1
|
|
if remaining_remote_calls_afc == 0:
|
|
logger.info(
|
|
'Reached max remote calls for automatic function calling.'
|
|
)
|
|
|
|
function_map = _extra_utils.get_function_map(
|
|
config, is_caller_method_async=True
|
|
)
|
|
|
|
if i == 1:
|
|
# First request gets a function call.
|
|
# Then get function response parts.
|
|
# Yield chunks only if there's no function response parts.
|
|
async for chunk in response: # type: ignore[attr-defined]
|
|
if not function_map:
|
|
yield chunk
|
|
else:
|
|
if (
|
|
not chunk.candidates
|
|
or not chunk.candidates[0].content
|
|
or not chunk.candidates[0].content.parts
|
|
):
|
|
break
|
|
func_response_parts = (
|
|
await _extra_utils.get_function_response_parts_async(
|
|
chunk, function_map
|
|
)
|
|
)
|
|
if not func_response_parts:
|
|
yield chunk
|
|
|
|
else:
|
|
# Second request and beyond, yield chunks.
|
|
async for chunk in response: # type: ignore[attr-defined]
|
|
|
|
if _extra_utils.should_append_afc_history(config):
|
|
chunk.automatic_function_calling_history = (
|
|
automatic_function_calling_history
|
|
)
|
|
yield chunk
|
|
if (
|
|
not chunk.candidates
|
|
or not chunk.candidates[0].content
|
|
or not chunk.candidates[0].content.parts
|
|
):
|
|
break
|
|
func_response_parts = _extra_utils.get_function_response_parts(
|
|
chunk, function_map
|
|
)
|
|
if not function_map:
|
|
break
|
|
|
|
if not func_response_parts:
|
|
break
|
|
|
|
# Append function response parts to contents for the next request.
|
|
func_call_content = chunk.candidates[0].content
|
|
func_response_content = types.Content(
|
|
role='user',
|
|
parts=func_response_parts,
|
|
)
|
|
contents = t.t_contents(self._api_client, contents)
|
|
if not automatic_function_calling_history:
|
|
automatic_function_calling_history.extend(contents)
|
|
if isinstance(contents, list) and func_call_content is not None:
|
|
contents.append(func_call_content)
|
|
contents.append(func_response_content)
|
|
if func_call_content is not None:
|
|
automatic_function_calling_history.append(func_call_content)
|
|
automatic_function_calling_history.append(func_response_content)
|
|
|
|
return async_generator(model, contents, config) # type: ignore[no-untyped-call, no-any-return]
|
|
|
|
async def edit_image(
|
|
self,
|
|
*,
|
|
model: str,
|
|
prompt: str,
|
|
reference_images: list[types._ReferenceImageAPIOrDict],
|
|
config: Optional[types.EditImageConfigOrDict] = None,
|
|
) -> types.EditImageResponse:
|
|
"""Edits an image based on a text description and configuration.
|
|
|
|
Args:
|
|
model (str): The model to use.
|
|
prompt (str): A text description of the edit to apply to the image.
|
|
reference_images (list[Union[RawReferenceImage, MaskReferenceImage,
|
|
ControlReferenceImage, StyleReferenceImage, SubjectReferenceImage]): The
|
|
reference images for editing.
|
|
config (EditImageConfig): Configuration for editing.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
from google.genai.types import RawReferenceImage, MaskReferenceImage
|
|
|
|
raw_ref_image = RawReferenceImage(
|
|
reference_id=1,
|
|
reference_image=types.Image.from_file(IMAGE_FILE_PATH),
|
|
)
|
|
|
|
mask_ref_image = MaskReferenceImage(
|
|
reference_id=2,
|
|
config=types.MaskReferenceConfig(
|
|
mask_mode='MASK_MODE_FOREGROUND',
|
|
mask_dilation=0.06,
|
|
),
|
|
)
|
|
response = await client.aio.models.edit_image(
|
|
model='imagen-3.0-capability-001',
|
|
prompt='man with dog',
|
|
reference_images=[raw_ref_image, mask_ref_image],
|
|
config=types.EditImageConfig(
|
|
edit_mode= "EDIT_MODE_INPAINT_INSERTION",
|
|
number_of_images= 1,
|
|
include_rai_reason= True,
|
|
)
|
|
)
|
|
response.generated_images[0].image.show()
|
|
# Shows a man with a dog instead of a cat.
|
|
"""
|
|
return await self._edit_image(
|
|
model=model,
|
|
prompt=prompt,
|
|
reference_images=reference_images,
|
|
config=config,
|
|
)
|
|
|
|
async def list(
|
|
self,
|
|
*,
|
|
config: Optional[types.ListModelsConfigOrDict] = None,
|
|
) -> AsyncPager[types.Model]:
|
|
"""Makes an API request to list the available models.
|
|
|
|
If `query_base` is set to True in the config or not set (default), the
|
|
API will return all available base models. If set to False, it will return
|
|
all tuned models.
|
|
|
|
Args:
|
|
config (ListModelsConfigOrDict): Configuration for retrieving models.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
response = await client.aio.models.list(config={'page_size': 5})
|
|
print(response.page)
|
|
# [Model(name='projects/./locations/./models/123', display_name='my_model'
|
|
|
|
response = await client.aio.models.list(
|
|
config={'page_size': 5, 'query_base': True}
|
|
)
|
|
print(response.page)
|
|
# [Model(name='publishers/google/models/gemini-2.0-flash-exp' ...
|
|
"""
|
|
|
|
config = (
|
|
types._ListModelsParameters(config=config).config
|
|
or types.ListModelsConfig()
|
|
)
|
|
if config.query_base is None:
|
|
config.query_base = True
|
|
if self._api_client.vertexai:
|
|
config = config.copy()
|
|
if not config.query_base:
|
|
# Filter for tuning jobs artifacts by labels.
|
|
filter_value = config.filter
|
|
config.filter = (
|
|
filter_value + '&filter=labels.tune-type:*'
|
|
if filter_value
|
|
else 'labels.tune-type:*'
|
|
)
|
|
return AsyncPager(
|
|
'models',
|
|
self._list,
|
|
await self._list(config=config),
|
|
config,
|
|
)
|
|
|
|
async def generate_images(
|
|
self,
|
|
*,
|
|
model: str,
|
|
prompt: str,
|
|
config: Optional[types.GenerateImagesConfigOrDict] = None,
|
|
) -> types.GenerateImagesResponse:
|
|
"""Generates images based on a text description and configuration.
|
|
|
|
Args:
|
|
model (str): The model to use.
|
|
prompt (str): A text description of the images to generate.
|
|
config (GenerateImagesConfig): Configuration for generation.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
response = await client.aio.models.generate_images(
|
|
model='imagen-3.0-generate-002',
|
|
prompt='Man with a dog',
|
|
config=types.GenerateImagesConfig(
|
|
number_of_images= 1,
|
|
include_rai_reason= True,
|
|
)
|
|
)
|
|
response.generated_images[0].image.show()
|
|
# Shows a man with a dog.
|
|
"""
|
|
api_response = await self._generate_images(
|
|
model=model,
|
|
prompt=prompt,
|
|
config=config,
|
|
)
|
|
positive_prompt_safety_attributes = None
|
|
generated_images = []
|
|
if not api_response or not api_response.generated_images:
|
|
return api_response
|
|
|
|
for generated_image in api_response.generated_images:
|
|
if (
|
|
generated_image.safety_attributes
|
|
and generated_image.safety_attributes.content_type
|
|
== 'Positive Prompt'
|
|
):
|
|
positive_prompt_safety_attributes = generated_image.safety_attributes
|
|
else:
|
|
generated_images.append(generated_image)
|
|
|
|
response = types.GenerateImagesResponse(
|
|
generated_images=generated_images,
|
|
positive_prompt_safety_attributes=positive_prompt_safety_attributes,
|
|
)
|
|
return response
|
|
|
|
async def upscale_image(
|
|
self,
|
|
*,
|
|
model: str,
|
|
image: types.ImageOrDict,
|
|
upscale_factor: str,
|
|
config: Optional[types.UpscaleImageConfigOrDict] = None,
|
|
) -> types.UpscaleImageResponse:
|
|
"""Makes an API request to upscale a provided image.
|
|
|
|
Args:
|
|
model (str): The model to use.
|
|
image (Image): The input image for upscaling.
|
|
upscale_factor (str): The factor to upscale the image (x2 or x4).
|
|
config (UpscaleImageConfig): Configuration for upscaling.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
from google.genai.types import Image
|
|
|
|
IMAGE_FILE_PATH="my-image.png"
|
|
response = await client.aio.models.upscale_image(
|
|
model='imagen-3.0-generate-001',
|
|
image=types.Image.from_file(IMAGE_FILE_PATH),
|
|
upscale_factor='x2',
|
|
)
|
|
response.generated_images[0].image.show()
|
|
# Opens my-image.png which is upscaled by a factor of 2.
|
|
"""
|
|
|
|
# Validate config.
|
|
types.UpscaleImageParameters(
|
|
model=model,
|
|
image=image,
|
|
upscale_factor=upscale_factor,
|
|
config=config,
|
|
)
|
|
|
|
# Convert to API config.
|
|
config = config or {}
|
|
if isinstance(config, types.UpscaleImageConfig):
|
|
config_dct = config.model_dump()
|
|
else:
|
|
config_dct = dict(config)
|
|
api_config = types._UpscaleImageAPIConfigDict(
|
|
http_options=config_dct.get('http_options', None),
|
|
include_rai_reason=config_dct.get('include_rai_reason', None),
|
|
output_mime_type=config_dct.get('output_mime_type', None),
|
|
output_compression_quality=config_dct.get(
|
|
'output_compression_quality', None
|
|
),
|
|
) # pylint: disable=protected-access
|
|
|
|
# Provide default values through API config.
|
|
api_config['mode'] = 'upscale'
|
|
api_config['number_of_images'] = 1
|
|
|
|
return await self._upscale_image(
|
|
model=model,
|
|
image=image,
|
|
upscale_factor=upscale_factor,
|
|
config=api_config,
|
|
)
|