Daniel Bedeleanu
ea49cd6e4a
- Create backend/services/image_storage.py with 4 core functions:
- sanitize_filename(): remove unsafe chars, limit to 255 chars, convert to lowercase
- get_unique_filename(): handle collisions with UUID suffix (format: {name}_{uuid8}_{variant}.jpg)
- ensure_image_directories(): create /images/ root and category subdirs on startup
- save_image(): save bytes to /images/{category}/{filename}, returns relative path
- Create comprehensive test suite (22 tests) covering all functionality
- Integrate ensure_image_directories() into FastAPI startup event
- Directory structure: /images/{category}/{filename}
- Collision handling: auto-suffix with UUID if filename exists
- All tests passing, pathlib.Path for safe operations
2851 lines
86 KiB
Python
2851 lines
86 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 json
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import logging
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from typing import Any, 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 move_value_by_path as movev
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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.batches')
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def _AuthConfig_to_mldev(
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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, ['api_key']) is not None:
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setv(to_object, ['apiKey'], getv(from_object, ['api_key']))
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if getv(from_object, ['api_key_config']) is not None:
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raise ValueError('api_key_config parameter is not supported in Gemini API.')
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if getv(from_object, ['auth_type']) is not None:
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raise ValueError('auth_type parameter is not supported in Gemini API.')
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if getv(from_object, ['google_service_account_config']) is not None:
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raise ValueError(
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'google_service_account_config parameter is not supported in Gemini'
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' API.'
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)
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if getv(from_object, ['http_basic_auth_config']) is not None:
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raise ValueError(
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'http_basic_auth_config parameter is not supported in Gemini API.'
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)
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if getv(from_object, ['oauth_config']) is not None:
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raise ValueError('oauth_config parameter is not supported in Gemini API.')
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if getv(from_object, ['oidc_config']) is not None:
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raise ValueError('oidc_config parameter is not supported in Gemini API.')
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return to_object
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def _BatchJobDestination_from_mldev(
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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, ['responsesFile']) is not None:
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setv(to_object, ['file_name'], getv(from_object, ['responsesFile']))
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if getv(from_object, ['inlinedResponses', 'inlinedResponses']) is not None:
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setv(
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to_object,
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['inlined_responses'],
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[
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_InlinedResponse_from_mldev(item, to_object)
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for item in getv(
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from_object, ['inlinedResponses', 'inlinedResponses']
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)
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],
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)
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if (
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getv(from_object, ['inlinedEmbedContentResponses', 'inlinedResponses'])
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is not None
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):
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setv(
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to_object,
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['inlined_embed_content_responses'],
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[
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item
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for item in getv(
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from_object,
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['inlinedEmbedContentResponses', 'inlinedResponses'],
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)
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],
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)
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return to_object
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def _BatchJobDestination_from_vertex(
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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, ['predictionsFormat']) is not None:
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setv(to_object, ['format'], getv(from_object, ['predictionsFormat']))
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if getv(from_object, ['gcsDestination', 'outputUriPrefix']) is not None:
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setv(
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to_object,
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['gcs_uri'],
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getv(from_object, ['gcsDestination', 'outputUriPrefix']),
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)
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if getv(from_object, ['bigqueryDestination', 'outputUri']) is not None:
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setv(
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to_object,
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['bigquery_uri'],
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getv(from_object, ['bigqueryDestination', 'outputUri']),
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)
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return to_object
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def _BatchJobDestination_to_vertex(
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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, ['format']) is not None:
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setv(to_object, ['predictionsFormat'], getv(from_object, ['format']))
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if getv(from_object, ['gcs_uri']) is not None:
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setv(
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to_object,
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['gcsDestination', 'outputUriPrefix'],
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getv(from_object, ['gcs_uri']),
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)
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if getv(from_object, ['bigquery_uri']) is not None:
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setv(
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to_object,
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['bigqueryDestination', 'outputUri'],
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getv(from_object, ['bigquery_uri']),
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)
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if getv(from_object, ['file_name']) is not None:
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raise ValueError('file_name parameter is not supported in Vertex AI.')
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if getv(from_object, ['inlined_responses']) is not None:
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raise ValueError(
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'inlined_responses parameter is not supported in Vertex AI.'
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)
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if getv(from_object, ['inlined_embed_content_responses']) is not None:
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raise ValueError(
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'inlined_embed_content_responses parameter is not supported in'
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' Vertex AI.'
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)
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return to_object
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def _BatchJobSource_from_vertex(
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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, ['instancesFormat']) is not None:
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setv(to_object, ['format'], getv(from_object, ['instancesFormat']))
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if getv(from_object, ['gcsSource', 'uris']) is not None:
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setv(to_object, ['gcs_uri'], getv(from_object, ['gcsSource', 'uris']))
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if getv(from_object, ['bigquerySource', 'inputUri']) is not None:
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setv(
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to_object,
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['bigquery_uri'],
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getv(from_object, ['bigquerySource', 'inputUri']),
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)
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return to_object
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def _BatchJobSource_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, ['format']) is not None:
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raise ValueError('format parameter is not supported in Gemini API.')
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if getv(from_object, ['gcs_uri']) is not None:
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raise ValueError('gcs_uri parameter is not supported in Gemini API.')
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if getv(from_object, ['bigquery_uri']) is not None:
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raise ValueError('bigquery_uri parameter is not supported in Gemini API.')
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if getv(from_object, ['file_name']) is not None:
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setv(to_object, ['fileName'], getv(from_object, ['file_name']))
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if getv(from_object, ['inlined_requests']) is not None:
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setv(
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to_object,
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['requests', 'requests'],
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[
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_InlinedRequest_to_mldev(api_client, item, to_object)
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for item in getv(from_object, ['inlined_requests'])
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],
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)
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return to_object
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def _BatchJobSource_to_vertex(
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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, ['format']) is not None:
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setv(to_object, ['instancesFormat'], getv(from_object, ['format']))
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if getv(from_object, ['gcs_uri']) is not None:
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setv(to_object, ['gcsSource', 'uris'], getv(from_object, ['gcs_uri']))
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if getv(from_object, ['bigquery_uri']) is not None:
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setv(
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to_object,
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['bigquerySource', 'inputUri'],
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getv(from_object, ['bigquery_uri']),
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)
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if getv(from_object, ['file_name']) is not None:
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raise ValueError('file_name parameter is not supported in Vertex AI.')
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if getv(from_object, ['inlined_requests']) is not None:
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raise ValueError(
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'inlined_requests parameter is not supported in Vertex AI.'
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)
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return to_object
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def _BatchJob_from_mldev(
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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, ['name']) is not None:
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setv(to_object, ['name'], getv(from_object, ['name']))
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if getv(from_object, ['metadata', 'displayName']) is not None:
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setv(
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to_object,
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['display_name'],
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getv(from_object, ['metadata', 'displayName']),
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)
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if getv(from_object, ['metadata', 'state']) is not None:
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setv(
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to_object,
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['state'],
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t.t_job_state(getv(from_object, ['metadata', 'state'])),
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)
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if getv(from_object, ['metadata', 'createTime']) is not None:
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setv(
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to_object,
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['create_time'],
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getv(from_object, ['metadata', 'createTime']),
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)
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if getv(from_object, ['metadata', 'endTime']) is not None:
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setv(to_object, ['end_time'], getv(from_object, ['metadata', 'endTime']))
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if getv(from_object, ['metadata', 'updateTime']) is not None:
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setv(
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to_object,
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['update_time'],
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getv(from_object, ['metadata', 'updateTime']),
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)
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if getv(from_object, ['metadata', 'model']) is not None:
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setv(to_object, ['model'], getv(from_object, ['metadata', 'model']))
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if getv(from_object, ['metadata', 'output']) is not None:
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setv(
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to_object,
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['dest'],
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_BatchJobDestination_from_mldev(
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t.t_recv_batch_job_destination(
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getv(from_object, ['metadata', 'output'])
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),
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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 _BatchJob_from_vertex(
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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, ['name']) is not None:
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setv(to_object, ['name'], getv(from_object, ['name']))
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if getv(from_object, ['displayName']) is not None:
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setv(to_object, ['display_name'], getv(from_object, ['displayName']))
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if getv(from_object, ['state']) is not None:
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setv(to_object, ['state'], t.t_job_state(getv(from_object, ['state'])))
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if getv(from_object, ['error']) is not None:
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setv(to_object, ['error'], getv(from_object, ['error']))
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if getv(from_object, ['createTime']) is not None:
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setv(to_object, ['create_time'], getv(from_object, ['createTime']))
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if getv(from_object, ['startTime']) is not None:
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setv(to_object, ['start_time'], getv(from_object, ['startTime']))
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if getv(from_object, ['endTime']) is not None:
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setv(to_object, ['end_time'], getv(from_object, ['endTime']))
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if getv(from_object, ['updateTime']) is not None:
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setv(to_object, ['update_time'], getv(from_object, ['updateTime']))
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if getv(from_object, ['model']) is not None:
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setv(to_object, ['model'], getv(from_object, ['model']))
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if getv(from_object, ['inputConfig']) is not None:
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setv(
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to_object,
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['src'],
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_BatchJobSource_from_vertex(
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getv(from_object, ['inputConfig']), to_object
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),
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)
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if getv(from_object, ['outputConfig']) is not None:
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setv(
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to_object,
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['dest'],
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_BatchJobDestination_from_vertex(
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t.t_recv_batch_job_destination(getv(from_object, ['outputConfig'])),
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to_object,
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),
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)
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if getv(from_object, ['completionStats']) is not None:
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setv(
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to_object, ['completion_stats'], getv(from_object, ['completionStats'])
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)
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return to_object
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def _Blob_to_mldev(
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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, ['data']) is not None:
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setv(to_object, ['data'], getv(from_object, ['data']))
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if getv(from_object, ['display_name']) is not None:
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raise ValueError('display_name parameter is not supported in Gemini API.')
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if getv(from_object, ['mime_type']) is not None:
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setv(to_object, ['mimeType'], getv(from_object, ['mime_type']))
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return to_object
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def _CancelBatchJobParameters_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, ['name']) is not None:
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setv(
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to_object,
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['_url', 'name'],
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t.t_batch_job_name(api_client, getv(from_object, ['name'])),
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)
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return to_object
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|
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def _CancelBatchJobParameters_to_vertex(
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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, ['name']) is not None:
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setv(
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to_object,
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['_url', 'name'],
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t.t_batch_job_name(api_client, getv(from_object, ['name'])),
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)
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return to_object
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|
|
|
|
def _Candidate_from_mldev(
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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, ['content']) is not None:
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setv(to_object, ['content'], getv(from_object, ['content']))
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|
|
|
if getv(from_object, ['citationMetadata']) is not None:
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|
setv(
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to_object,
|
|
['citation_metadata'],
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_CitationMetadata_from_mldev(
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getv(from_object, ['citationMetadata']), to_object
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),
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)
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|
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|
if getv(from_object, ['tokenCount']) is not None:
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|
setv(to_object, ['token_count'], getv(from_object, ['tokenCount']))
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|
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|
if getv(from_object, ['finishReason']) is not None:
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|
setv(to_object, ['finish_reason'], getv(from_object, ['finishReason']))
|
|
|
|
if getv(from_object, ['groundingMetadata']) is not None:
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|
setv(
|
|
to_object,
|
|
['grounding_metadata'],
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getv(from_object, ['groundingMetadata']),
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)
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if getv(from_object, ['avgLogprobs']) is not None:
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setv(to_object, ['avg_logprobs'], getv(from_object, ['avgLogprobs']))
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|
|
|
if getv(from_object, ['index']) is not None:
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setv(to_object, ['index'], getv(from_object, ['index']))
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|
if getv(from_object, ['logprobsResult']) is not None:
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|
setv(to_object, ['logprobs_result'], getv(from_object, ['logprobsResult']))
|
|
|
|
if getv(from_object, ['safetyRatings']) is not None:
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|
setv(
|
|
to_object,
|
|
['safety_ratings'],
|
|
[item for item in getv(from_object, ['safetyRatings'])],
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|
)
|
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|
|
if getv(from_object, ['urlContextMetadata']) is not None:
|
|
setv(
|
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to_object,
|
|
['url_context_metadata'],
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getv(from_object, ['urlContextMetadata']),
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)
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return to_object
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|
|
|
|
|
def _CitationMetadata_from_mldev(
|
|
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'],
|
|
[item for item in getv(from_object, ['citationSources'])],
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)
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|
return to_object
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|
|
|
|
|
def _Content_to_mldev(
|
|
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_mldev(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 _CreateBatchJobConfig_to_mldev(
|
|
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,
|
|
['batch', 'displayName'],
|
|
getv(from_object, ['display_name']),
|
|
)
|
|
|
|
if getv(from_object, ['dest']) is not None:
|
|
raise ValueError('dest parameter is not supported in Gemini API.')
|
|
|
|
if getv(from_object, ['webhook_config']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['batch', 'webhookConfig'],
|
|
getv(from_object, ['webhook_config']),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _CreateBatchJobConfig_to_vertex(
|
|
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, ['dest']) is not None:
|
|
setv(
|
|
parent_object,
|
|
['outputConfig'],
|
|
_BatchJobDestination_to_vertex(
|
|
t.t_batch_job_destination(getv(from_object, ['dest'])), to_object
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['webhook_config']) is not None:
|
|
raise ValueError('webhook_config parameter is not supported in Vertex AI.')
|
|
|
|
return to_object
|
|
|
|
|
|
def _CreateBatchJobParameters_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, ['src']) is not None:
|
|
setv(
|
|
to_object,
|
|
['batch', 'inputConfig'],
|
|
_BatchJobSource_to_mldev(
|
|
api_client,
|
|
t.t_batch_job_source(api_client, getv(from_object, ['src'])),
|
|
to_object,
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['config']) is not None:
|
|
_CreateBatchJobConfig_to_mldev(getv(from_object, ['config']), to_object)
|
|
|
|
return to_object
|
|
|
|
|
|
def _CreateBatchJobParameters_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,
|
|
['model'],
|
|
t.t_model(api_client, getv(from_object, ['model'])),
|
|
)
|
|
|
|
if getv(from_object, ['src']) is not None:
|
|
setv(
|
|
to_object,
|
|
['inputConfig'],
|
|
_BatchJobSource_to_vertex(
|
|
t.t_batch_job_source(api_client, getv(from_object, ['src'])),
|
|
to_object,
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['config']) is not None:
|
|
_CreateBatchJobConfig_to_vertex(getv(from_object, ['config']), to_object)
|
|
|
|
return to_object
|
|
|
|
|
|
def _CreateEmbeddingsBatchJobConfig_to_mldev(
|
|
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,
|
|
['batch', 'displayName'],
|
|
getv(from_object, ['display_name']),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _CreateEmbeddingsBatchJobParameters_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, ['src']) is not None:
|
|
setv(
|
|
to_object,
|
|
['batch', 'inputConfig'],
|
|
_EmbeddingsBatchJobSource_to_mldev(
|
|
api_client, getv(from_object, ['src']), to_object
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['config']) is not None:
|
|
_CreateEmbeddingsBatchJobConfig_to_mldev(
|
|
getv(from_object, ['config']), to_object
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _DeleteBatchJobParameters_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, ['name']) is not None:
|
|
setv(
|
|
to_object,
|
|
['_url', 'name'],
|
|
t.t_batch_job_name(api_client, getv(from_object, ['name'])),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _DeleteBatchJobParameters_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, ['name']) is not None:
|
|
setv(
|
|
to_object,
|
|
['_url', 'name'],
|
|
t.t_batch_job_name(api_client, getv(from_object, ['name'])),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _DeleteResourceJob_from_mldev(
|
|
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, ['sdkHttpResponse']) is not None:
|
|
setv(
|
|
to_object, ['sdk_http_response'], getv(from_object, ['sdkHttpResponse'])
|
|
)
|
|
|
|
if getv(from_object, ['name']) is not None:
|
|
setv(to_object, ['name'], getv(from_object, ['name']))
|
|
|
|
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']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _DeleteResourceJob_from_vertex(
|
|
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, ['sdkHttpResponse']) is not None:
|
|
setv(
|
|
to_object, ['sdk_http_response'], getv(from_object, ['sdkHttpResponse'])
|
|
)
|
|
|
|
if getv(from_object, ['name']) is not None:
|
|
setv(to_object, ['name'], getv(from_object, ['name']))
|
|
|
|
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']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _EmbedContentBatch_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, ['contents']) is not None:
|
|
setv(
|
|
to_object,
|
|
['requests[]', 'request', 'content'],
|
|
[
|
|
item
|
|
for item in t.t_contents_for_embed(
|
|
api_client, getv(from_object, ['contents'])
|
|
)
|
|
],
|
|
)
|
|
|
|
if getv(from_object, ['config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['_self'],
|
|
_EmbedContentConfig_to_mldev(getv(from_object, ['config']), to_object),
|
|
)
|
|
movev(to_object, {'requests[].*': 'requests[].request.*'})
|
|
return to_object
|
|
|
|
|
|
def _EmbedContentConfig_to_mldev(
|
|
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.')
|
|
|
|
if getv(from_object, ['document_ocr']) is not None:
|
|
raise ValueError('document_ocr parameter is not supported in Gemini API.')
|
|
|
|
if getv(from_object, ['audio_track_extraction']) is not None:
|
|
raise ValueError(
|
|
'audio_track_extraction parameter is not supported in Gemini API.'
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _EmbeddingsBatchJobSource_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, ['file_name']) is not None:
|
|
setv(to_object, ['file_name'], getv(from_object, ['file_name']))
|
|
|
|
if getv(from_object, ['inlined_requests']) is not None:
|
|
setv(
|
|
to_object,
|
|
['requests'],
|
|
_EmbedContentBatch_to_mldev(
|
|
api_client, getv(from_object, ['inlined_requests']), to_object
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _FileData_to_mldev(
|
|
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:
|
|
raise ValueError('display_name parameter is not supported in Gemini API.')
|
|
|
|
if getv(from_object, ['file_uri']) is not None:
|
|
setv(to_object, ['fileUri'], getv(from_object, ['file_uri']))
|
|
|
|
if getv(from_object, ['mime_type']) is not None:
|
|
setv(to_object, ['mimeType'], getv(from_object, ['mime_type']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _FunctionCall_to_mldev(
|
|
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, ['id']) is not None:
|
|
setv(to_object, ['id'], getv(from_object, ['id']))
|
|
|
|
if getv(from_object, ['args']) is not None:
|
|
setv(to_object, ['args'], getv(from_object, ['args']))
|
|
|
|
if getv(from_object, ['name']) is not None:
|
|
setv(to_object, ['name'], getv(from_object, ['name']))
|
|
|
|
if getv(from_object, ['partial_args']) is not None:
|
|
raise ValueError('partial_args parameter is not supported in Gemini API.')
|
|
|
|
if getv(from_object, ['will_continue']) is not None:
|
|
raise ValueError('will_continue parameter is not supported in Gemini API.')
|
|
|
|
return to_object
|
|
|
|
|
|
def _FunctionCallingConfig_to_mldev(
|
|
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, ['allowed_function_names']) is not None:
|
|
setv(
|
|
to_object,
|
|
['allowedFunctionNames'],
|
|
getv(from_object, ['allowed_function_names']),
|
|
)
|
|
|
|
if getv(from_object, ['mode']) is not None:
|
|
setv(to_object, ['mode'], getv(from_object, ['mode']))
|
|
|
|
if getv(from_object, ['stream_function_call_arguments']) is not None:
|
|
raise ValueError(
|
|
'stream_function_call_arguments parameter is not supported in Gemini'
|
|
' API.'
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _GenerateContentConfig_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:
|
|
setv(
|
|
parent_object,
|
|
['systemInstruction'],
|
|
_Content_to_mldev(
|
|
t.t_content(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'],
|
|
t.t_schema(api_client, getv(from_object, ['response_schema'])),
|
|
)
|
|
|
|
if getv(from_object, ['response_json_schema']) is not None:
|
|
setv(
|
|
to_object,
|
|
['responseJsonSchema'],
|
|
getv(from_object, ['response_json_schema']),
|
|
)
|
|
|
|
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(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(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(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'],
|
|
t.t_speech_config(getv(from_object, ['speech_config'])),
|
|
)
|
|
|
|
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'], getv(from_object, ['thinking_config']))
|
|
|
|
if getv(from_object, ['image_config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['imageConfig'],
|
|
_ImageConfig_to_mldev(getv(from_object, ['image_config']), to_object),
|
|
)
|
|
|
|
if getv(from_object, ['enable_enhanced_civic_answers']) is not None:
|
|
setv(
|
|
to_object,
|
|
['enableEnhancedCivicAnswers'],
|
|
getv(from_object, ['enable_enhanced_civic_answers']),
|
|
)
|
|
|
|
if getv(from_object, ['model_armor_config']) is not None:
|
|
raise ValueError(
|
|
'model_armor_config parameter is not supported in Gemini API.'
|
|
)
|
|
|
|
if getv(from_object, ['service_tier']) is not None:
|
|
setv(parent_object, ['serviceTier'], getv(from_object, ['service_tier']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _GenerateContentResponse_from_mldev(
|
|
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, ['sdkHttpResponse']) is not None:
|
|
setv(
|
|
to_object, ['sdk_http_response'], getv(from_object, ['sdkHttpResponse'])
|
|
)
|
|
|
|
if getv(from_object, ['candidates']) is not None:
|
|
setv(
|
|
to_object,
|
|
['candidates'],
|
|
[
|
|
_Candidate_from_mldev(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, ['responseId']) is not None:
|
|
setv(to_object, ['response_id'], getv(from_object, ['responseId']))
|
|
|
|
if getv(from_object, ['usageMetadata']) is not None:
|
|
setv(to_object, ['usage_metadata'], getv(from_object, ['usageMetadata']))
|
|
|
|
if getv(from_object, ['modelStatus']) is not None:
|
|
setv(to_object, ['model_status'], getv(from_object, ['modelStatus']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _GetBatchJobParameters_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, ['name']) is not None:
|
|
setv(
|
|
to_object,
|
|
['_url', 'name'],
|
|
t.t_batch_job_name(api_client, getv(from_object, ['name'])),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _GetBatchJobParameters_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, ['name']) is not None:
|
|
setv(
|
|
to_object,
|
|
['_url', 'name'],
|
|
t.t_batch_job_name(api_client, getv(from_object, ['name'])),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _GoogleMaps_to_mldev(
|
|
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, ['auth_config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['authConfig'],
|
|
_AuthConfig_to_mldev(getv(from_object, ['auth_config']), to_object),
|
|
)
|
|
|
|
if getv(from_object, ['enable_widget']) is not None:
|
|
setv(to_object, ['enableWidget'], getv(from_object, ['enable_widget']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _GoogleSearch_to_mldev(
|
|
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, ['search_types']) is not None:
|
|
setv(to_object, ['searchTypes'], getv(from_object, ['search_types']))
|
|
|
|
if getv(from_object, ['blocking_confidence']) is not None:
|
|
raise ValueError(
|
|
'blocking_confidence parameter is not supported in Gemini API.'
|
|
)
|
|
|
|
if getv(from_object, ['exclude_domains']) is not None:
|
|
raise ValueError(
|
|
'exclude_domains parameter is not supported in Gemini API.'
|
|
)
|
|
|
|
if getv(from_object, ['time_range_filter']) is not None:
|
|
setv(
|
|
to_object, ['timeRangeFilter'], getv(from_object, ['time_range_filter'])
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _ImageConfig_to_mldev(
|
|
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, ['aspect_ratio']) is not None:
|
|
setv(to_object, ['aspectRatio'], getv(from_object, ['aspect_ratio']))
|
|
|
|
if getv(from_object, ['image_size']) is not None:
|
|
setv(to_object, ['imageSize'], getv(from_object, ['image_size']))
|
|
|
|
if getv(from_object, ['person_generation']) is not None:
|
|
raise ValueError(
|
|
'person_generation parameter is not supported in Gemini API.'
|
|
)
|
|
|
|
if getv(from_object, ['prominent_people']) is not None:
|
|
raise ValueError(
|
|
'prominent_people parameter is not supported in Gemini API.'
|
|
)
|
|
|
|
if getv(from_object, ['output_mime_type']) is not None:
|
|
raise ValueError(
|
|
'output_mime_type parameter is not supported in Gemini API.'
|
|
)
|
|
|
|
if getv(from_object, ['output_compression_quality']) is not None:
|
|
raise ValueError(
|
|
'output_compression_quality parameter is not supported in Gemini API.'
|
|
)
|
|
|
|
if getv(from_object, ['image_output_options']) is not None:
|
|
raise ValueError(
|
|
'image_output_options parameter is not supported in Gemini API.'
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _InlinedRequest_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,
|
|
['request', 'model'],
|
|
t.t_model(api_client, getv(from_object, ['model'])),
|
|
)
|
|
|
|
if getv(from_object, ['contents']) is not None:
|
|
setv(
|
|
to_object,
|
|
['request', 'contents'],
|
|
[
|
|
_Content_to_mldev(item, to_object)
|
|
for item in t.t_contents(getv(from_object, ['contents']))
|
|
],
|
|
)
|
|
|
|
if getv(from_object, ['metadata']) is not None:
|
|
setv(to_object, ['metadata'], getv(from_object, ['metadata']))
|
|
|
|
if getv(from_object, ['config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['request', 'generationConfig'],
|
|
_GenerateContentConfig_to_mldev(
|
|
api_client,
|
|
getv(from_object, ['config']),
|
|
getv(to_object, ['request'], default_value={}),
|
|
),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _InlinedResponse_from_mldev(
|
|
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'],
|
|
_GenerateContentResponse_from_mldev(
|
|
getv(from_object, ['response']), to_object
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['metadata']) is not None:
|
|
setv(to_object, ['metadata'], getv(from_object, ['metadata']))
|
|
|
|
if getv(from_object, ['error']) is not None:
|
|
setv(to_object, ['error'], getv(from_object, ['error']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _ListBatchJobsConfig_to_mldev(
|
|
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:
|
|
raise ValueError('filter parameter is not supported in Gemini API.')
|
|
|
|
return to_object
|
|
|
|
|
|
def _ListBatchJobsConfig_to_vertex(
|
|
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']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _ListBatchJobsParameters_to_mldev(
|
|
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:
|
|
_ListBatchJobsConfig_to_mldev(getv(from_object, ['config']), to_object)
|
|
|
|
return to_object
|
|
|
|
|
|
def _ListBatchJobsParameters_to_vertex(
|
|
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:
|
|
_ListBatchJobsConfig_to_vertex(getv(from_object, ['config']), to_object)
|
|
|
|
return to_object
|
|
|
|
|
|
def _ListBatchJobsResponse_from_mldev(
|
|
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, ['sdkHttpResponse']) is not None:
|
|
setv(
|
|
to_object, ['sdk_http_response'], getv(from_object, ['sdkHttpResponse'])
|
|
)
|
|
|
|
if getv(from_object, ['nextPageToken']) is not None:
|
|
setv(to_object, ['next_page_token'], getv(from_object, ['nextPageToken']))
|
|
|
|
if getv(from_object, ['operations']) is not None:
|
|
setv(
|
|
to_object,
|
|
['batch_jobs'],
|
|
[
|
|
_BatchJob_from_mldev(item, to_object)
|
|
for item in getv(from_object, ['operations'])
|
|
],
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _ListBatchJobsResponse_from_vertex(
|
|
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, ['sdkHttpResponse']) is not None:
|
|
setv(
|
|
to_object, ['sdk_http_response'], getv(from_object, ['sdkHttpResponse'])
|
|
)
|
|
|
|
if getv(from_object, ['nextPageToken']) is not None:
|
|
setv(to_object, ['next_page_token'], getv(from_object, ['nextPageToken']))
|
|
|
|
if getv(from_object, ['batchPredictionJobs']) is not None:
|
|
setv(
|
|
to_object,
|
|
['batch_jobs'],
|
|
[
|
|
_BatchJob_from_vertex(item, to_object)
|
|
for item in getv(from_object, ['batchPredictionJobs'])
|
|
],
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _Part_to_mldev(
|
|
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, ['media_resolution']) is not None:
|
|
setv(
|
|
to_object, ['mediaResolution'], getv(from_object, ['media_resolution'])
|
|
)
|
|
|
|
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'],
|
|
_FileData_to_mldev(getv(from_object, ['file_data']), to_object),
|
|
)
|
|
|
|
if getv(from_object, ['function_call']) is not None:
|
|
setv(
|
|
to_object,
|
|
['functionCall'],
|
|
_FunctionCall_to_mldev(getv(from_object, ['function_call']), to_object),
|
|
)
|
|
|
|
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'],
|
|
_Blob_to_mldev(getv(from_object, ['inline_data']), to_object),
|
|
)
|
|
|
|
if getv(from_object, ['text']) is not None:
|
|
setv(to_object, ['text'], getv(from_object, ['text']))
|
|
|
|
if getv(from_object, ['thought']) is not None:
|
|
setv(to_object, ['thought'], getv(from_object, ['thought']))
|
|
|
|
if getv(from_object, ['thought_signature']) is not None:
|
|
setv(
|
|
to_object,
|
|
['thoughtSignature'],
|
|
getv(from_object, ['thought_signature']),
|
|
)
|
|
|
|
if getv(from_object, ['video_metadata']) is not None:
|
|
setv(to_object, ['videoMetadata'], getv(from_object, ['video_metadata']))
|
|
|
|
if getv(from_object, ['tool_call']) is not None:
|
|
setv(to_object, ['toolCall'], getv(from_object, ['tool_call']))
|
|
|
|
if getv(from_object, ['tool_response']) is not None:
|
|
setv(to_object, ['toolResponse'], getv(from_object, ['tool_response']))
|
|
|
|
if getv(from_object, ['part_metadata']) is not None:
|
|
setv(to_object, ['partMetadata'], getv(from_object, ['part_metadata']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _SafetySetting_to_mldev(
|
|
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, ['category']) is not None:
|
|
setv(to_object, ['category'], getv(from_object, ['category']))
|
|
|
|
if getv(from_object, ['method']) is not None:
|
|
raise ValueError('method parameter is not supported in Gemini API.')
|
|
|
|
if getv(from_object, ['threshold']) is not None:
|
|
setv(to_object, ['threshold'], getv(from_object, ['threshold']))
|
|
|
|
return to_object
|
|
|
|
|
|
def _ToolConfig_to_mldev(
|
|
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, ['retrieval_config']) is not None:
|
|
setv(
|
|
to_object, ['retrievalConfig'], getv(from_object, ['retrieval_config'])
|
|
)
|
|
|
|
if getv(from_object, ['function_calling_config']) is not None:
|
|
setv(
|
|
to_object,
|
|
['functionCallingConfig'],
|
|
_FunctionCallingConfig_to_mldev(
|
|
getv(from_object, ['function_calling_config']), to_object
|
|
),
|
|
)
|
|
|
|
if getv(from_object, ['include_server_side_tool_invocations']) is not None:
|
|
setv(
|
|
to_object,
|
|
['includeServerSideToolInvocations'],
|
|
getv(from_object, ['include_server_side_tool_invocations']),
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
def _Tool_to_mldev(
|
|
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, ['retrieval']) is not None:
|
|
raise ValueError('retrieval parameter is not supported in Gemini API.')
|
|
|
|
if getv(from_object, ['computer_use']) is not None:
|
|
setv(to_object, ['computerUse'], getv(from_object, ['computer_use']))
|
|
|
|
if getv(from_object, ['file_search']) is not None:
|
|
setv(to_object, ['fileSearch'], getv(from_object, ['file_search']))
|
|
|
|
if getv(from_object, ['google_search']) is not None:
|
|
setv(
|
|
to_object,
|
|
['googleSearch'],
|
|
_GoogleSearch_to_mldev(getv(from_object, ['google_search']), to_object),
|
|
)
|
|
|
|
if getv(from_object, ['google_maps']) is not None:
|
|
setv(
|
|
to_object,
|
|
['googleMaps'],
|
|
_GoogleMaps_to_mldev(getv(from_object, ['google_maps']), to_object),
|
|
)
|
|
|
|
if getv(from_object, ['code_execution']) is not None:
|
|
setv(to_object, ['codeExecution'], getv(from_object, ['code_execution']))
|
|
|
|
if getv(from_object, ['enterprise_web_search']) is not None:
|
|
raise ValueError(
|
|
'enterprise_web_search parameter is not supported in Gemini API.'
|
|
)
|
|
|
|
if getv(from_object, ['function_declarations']) is not None:
|
|
setv(
|
|
to_object,
|
|
['functionDeclarations'],
|
|
[item for item in getv(from_object, ['function_declarations'])],
|
|
)
|
|
|
|
if getv(from_object, ['google_search_retrieval']) is not None:
|
|
setv(
|
|
to_object,
|
|
['googleSearchRetrieval'],
|
|
getv(from_object, ['google_search_retrieval']),
|
|
)
|
|
|
|
if getv(from_object, ['parallel_ai_search']) is not None:
|
|
raise ValueError(
|
|
'parallel_ai_search parameter is not supported in Gemini API.'
|
|
)
|
|
|
|
if getv(from_object, ['url_context']) is not None:
|
|
setv(to_object, ['urlContext'], getv(from_object, ['url_context']))
|
|
|
|
if getv(from_object, ['mcp_servers']) is not None:
|
|
setv(
|
|
to_object,
|
|
['mcpServers'],
|
|
[item for item in getv(from_object, ['mcp_servers'])],
|
|
)
|
|
|
|
return to_object
|
|
|
|
|
|
class Batches(_api_module.BaseModule):
|
|
|
|
def _create(
|
|
self,
|
|
*,
|
|
model: Optional[str] = None,
|
|
src: Union[types.BatchJobSourceUnion, types.BatchJobSourceUnionDict],
|
|
config: Optional[types.CreateBatchJobConfigOrDict] = None,
|
|
) -> types.BatchJob:
|
|
parameter_model = types._CreateBatchJobParameters(
|
|
model=model,
|
|
src=src,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _CreateBatchJobParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = 'batchPredictionJobs'.format_map(request_url_dict)
|
|
else:
|
|
path = 'batchPredictionJobs'
|
|
else:
|
|
request_dict = _CreateBatchJobParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:batchGenerateContent'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:batchGenerateContent'
|
|
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 = self._api_client.request(
|
|
'post', path, request_dict, http_options
|
|
)
|
|
|
|
response_dict = {} if not response.body else json.loads(response.body)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _BatchJob_from_vertex(response_dict)
|
|
|
|
if not self._api_client.vertexai:
|
|
response_dict = _BatchJob_from_mldev(response_dict)
|
|
|
|
return_value = types.BatchJob._from_response(
|
|
response=response_dict,
|
|
kwargs={
|
|
'config': {
|
|
'response_schema': getattr(
|
|
parameter_model.config, 'response_schema', None
|
|
),
|
|
'response_json_schema': getattr(
|
|
parameter_model.config, 'response_json_schema', None
|
|
),
|
|
'include_all_fields': getattr(
|
|
parameter_model.config, 'include_all_fields', None
|
|
),
|
|
}
|
|
}
|
|
if getattr(parameter_model, 'config', None)
|
|
else {},
|
|
)
|
|
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
def _create_embeddings(
|
|
self,
|
|
*,
|
|
model: Optional[str] = None,
|
|
src: types.EmbeddingsBatchJobSourceOrDict,
|
|
config: Optional[types.CreateEmbeddingsBatchJobConfigOrDict] = None,
|
|
) -> types.BatchJob:
|
|
parameter_model = types._CreateEmbeddingsBatchJobParameters(
|
|
model=model,
|
|
src=src,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
if self._api_client.vertexai:
|
|
raise ValueError(
|
|
'This method is only supported in the Gemini Developer client.'
|
|
)
|
|
else:
|
|
request_dict = _CreateEmbeddingsBatchJobParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:asyncBatchEmbedContent'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:asyncBatchEmbedContent'
|
|
|
|
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 = self._api_client.request(
|
|
'post', path, request_dict, http_options
|
|
)
|
|
|
|
response_dict = {} if not response.body else json.loads(response.body)
|
|
|
|
if not self._api_client.vertexai:
|
|
response_dict = _BatchJob_from_mldev(response_dict)
|
|
|
|
return_value = types.BatchJob._from_response(
|
|
response=response_dict,
|
|
kwargs={
|
|
'config': {
|
|
'response_schema': getattr(
|
|
parameter_model.config, 'response_schema', None
|
|
),
|
|
'response_json_schema': getattr(
|
|
parameter_model.config, 'response_json_schema', None
|
|
),
|
|
'include_all_fields': getattr(
|
|
parameter_model.config, 'include_all_fields', None
|
|
),
|
|
}
|
|
}
|
|
if getattr(parameter_model, 'config', None)
|
|
else {},
|
|
)
|
|
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
def get(
|
|
self, *, name: str, config: Optional[types.GetBatchJobConfigOrDict] = None
|
|
) -> types.BatchJob:
|
|
"""Gets a batch job.
|
|
|
|
Args:
|
|
name (str): A fully-qualified BatchJob resource name or ID.
|
|
Example: "projects/.../locations/.../batchPredictionJobs/456" or "456"
|
|
when project and location are initialized in the Vertex AI client. Or
|
|
"batches/abc" using the Gemini Developer AI client.
|
|
|
|
Returns:
|
|
A BatchJob object that contains details about the batch job.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
batch_job = client.batches.get(name='123456789')
|
|
print(f"Batch job: {batch_job.name}, state {batch_job.state}")
|
|
"""
|
|
|
|
parameter_model = types._GetBatchJobParameters(
|
|
name=name,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _GetBatchJobParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = 'batchPredictionJobs/{name}'.format_map(request_url_dict)
|
|
else:
|
|
path = 'batchPredictionJobs/{name}'
|
|
else:
|
|
request_dict = _GetBatchJobParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = 'batches/{name}'.format_map(request_url_dict)
|
|
else:
|
|
path = 'batches/{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 = self._api_client.request('get', path, request_dict, http_options)
|
|
|
|
response_dict = {} if not response.body else json.loads(response.body)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _BatchJob_from_vertex(response_dict)
|
|
|
|
if not self._api_client.vertexai:
|
|
response_dict = _BatchJob_from_mldev(response_dict)
|
|
|
|
return_value = types.BatchJob._from_response(
|
|
response=response_dict,
|
|
kwargs={
|
|
'config': {
|
|
'response_schema': getattr(
|
|
parameter_model.config, 'response_schema', None
|
|
),
|
|
'response_json_schema': getattr(
|
|
parameter_model.config, 'response_json_schema', None
|
|
),
|
|
'include_all_fields': getattr(
|
|
parameter_model.config, 'include_all_fields', None
|
|
),
|
|
}
|
|
}
|
|
if getattr(parameter_model, 'config', None)
|
|
else {},
|
|
)
|
|
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
def cancel(
|
|
self,
|
|
*,
|
|
name: str,
|
|
config: Optional[types.CancelBatchJobConfigOrDict] = None,
|
|
) -> None:
|
|
"""Cancels a batch job.
|
|
|
|
Only available for batch jobs that are running or pending.
|
|
|
|
Args:
|
|
name (str): A fully-qualified BatchJob resource name or ID.
|
|
Example: "projects/.../locations/.../batchPredictionJobs/456" or "456"
|
|
when project and location are initialized in the Vertex AI client. Or
|
|
"batches/abc" using the Gemini Developer AI client.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
client.batches.cancel(name='123456789')
|
|
"""
|
|
|
|
parameter_model = types._CancelBatchJobParameters(
|
|
name=name,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _CancelBatchJobParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = 'batchPredictionJobs/{name}:cancel'.format_map(request_url_dict)
|
|
else:
|
|
path = 'batchPredictionJobs/{name}:cancel'
|
|
else:
|
|
request_dict = _CancelBatchJobParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = 'batches/{name}:cancel'.format_map(request_url_dict)
|
|
else:
|
|
path = 'batches/{name}:cancel'
|
|
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)
|
|
|
|
self._api_client.request('post', path, request_dict, http_options)
|
|
|
|
def _list(
|
|
self, *, config: Optional[types.ListBatchJobsConfigOrDict] = None
|
|
) -> types.ListBatchJobsResponse:
|
|
parameter_model = types._ListBatchJobsParameters(
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _ListBatchJobsParameters_to_vertex(parameter_model)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = 'batchPredictionJobs'.format_map(request_url_dict)
|
|
else:
|
|
path = 'batchPredictionJobs'
|
|
else:
|
|
request_dict = _ListBatchJobsParameters_to_mldev(parameter_model)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = 'batches'.format_map(request_url_dict)
|
|
else:
|
|
path = 'batches'
|
|
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 = self._api_client.request('get', path, request_dict, http_options)
|
|
|
|
response_dict = {} if not response.body else json.loads(response.body)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _ListBatchJobsResponse_from_vertex(response_dict)
|
|
|
|
if not self._api_client.vertexai:
|
|
response_dict = _ListBatchJobsResponse_from_mldev(response_dict)
|
|
|
|
return_value = types.ListBatchJobsResponse._from_response(
|
|
response=response_dict,
|
|
kwargs={
|
|
'config': {
|
|
'response_schema': getattr(
|
|
parameter_model.config, 'response_schema', None
|
|
),
|
|
'response_json_schema': getattr(
|
|
parameter_model.config, 'response_json_schema', None
|
|
),
|
|
'include_all_fields': getattr(
|
|
parameter_model.config, 'include_all_fields', None
|
|
),
|
|
}
|
|
}
|
|
if getattr(parameter_model, 'config', None)
|
|
else {},
|
|
)
|
|
return_value.sdk_http_response = types.HttpResponse(
|
|
headers=response.headers
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
def delete(
|
|
self,
|
|
*,
|
|
name: str,
|
|
config: Optional[types.DeleteBatchJobConfigOrDict] = None,
|
|
) -> types.DeleteResourceJob:
|
|
"""Deletes a batch job.
|
|
|
|
Args:
|
|
name (str): A fully-qualified BatchJob resource name or ID.
|
|
Example: "projects/.../locations/.../batchPredictionJobs/456" or "456"
|
|
when project and location are initialized in the client.
|
|
|
|
Returns:
|
|
A DeleteResourceJob object that shows the status of the deletion.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
client.batches.delete(name='123456789')
|
|
"""
|
|
|
|
parameter_model = types._DeleteBatchJobParameters(
|
|
name=name,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _DeleteBatchJobParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = 'batchPredictionJobs/{name}'.format_map(request_url_dict)
|
|
else:
|
|
path = 'batchPredictionJobs/{name}'
|
|
else:
|
|
request_dict = _DeleteBatchJobParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = 'batches/{name}'.format_map(request_url_dict)
|
|
else:
|
|
path = 'batches/{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 = self._api_client.request(
|
|
'delete', path, request_dict, http_options
|
|
)
|
|
|
|
response_dict = {} if not response.body else json.loads(response.body)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _DeleteResourceJob_from_vertex(response_dict)
|
|
|
|
if not self._api_client.vertexai:
|
|
response_dict = _DeleteResourceJob_from_mldev(response_dict)
|
|
|
|
return_value = types.DeleteResourceJob._from_response(
|
|
response=response_dict,
|
|
kwargs={
|
|
'config': {
|
|
'response_schema': getattr(
|
|
parameter_model.config, 'response_schema', None
|
|
),
|
|
'response_json_schema': getattr(
|
|
parameter_model.config, 'response_json_schema', None
|
|
),
|
|
'include_all_fields': getattr(
|
|
parameter_model.config, 'include_all_fields', None
|
|
),
|
|
}
|
|
}
|
|
if getattr(parameter_model, 'config', None)
|
|
else {},
|
|
)
|
|
return_value.sdk_http_response = types.HttpResponse(
|
|
headers=response.headers
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
def create(
|
|
self,
|
|
*,
|
|
model: str,
|
|
src: types.BatchJobSourceUnionDict,
|
|
config: Optional[types.CreateBatchJobConfigOrDict] = None,
|
|
) -> types.BatchJob:
|
|
"""Creates a batch job.
|
|
|
|
Args:
|
|
model (str): The model to use for the batch job.
|
|
src: The source of the batch job. Currently Vertex AI supports GCS URI(-s)
|
|
or BigQuery URI. Example: "gs://path/to/input/data" or
|
|
"bq://projectId.bqDatasetId.bqTableId". Gemini Developer API supports
|
|
List of inlined_request, or file name. Example: "files/file_name".
|
|
config (CreateBatchJobConfig): Optional configuration for the batch job.
|
|
|
|
Returns:
|
|
A BatchJob object that contains details about the batch job.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
batch_job = client.batches.create(
|
|
model="gemini-2.0-flash-001",
|
|
src="gs://path/to/input/data",
|
|
)
|
|
print(batch_job.state)
|
|
"""
|
|
src = t.t_batch_job_source(self._api_client, src)
|
|
|
|
# Convert all dicts to Pydantic objects.
|
|
parameter_model = types._CreateBatchJobParameters(
|
|
model=model,
|
|
src=src,
|
|
config=config,
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
config = _extra_utils.format_destination(src, parameter_model.config)
|
|
return self._create(model=model, src=src, config=config)
|
|
else:
|
|
return self._create(model=model, src=src, config=config)
|
|
|
|
def create_embeddings(
|
|
self,
|
|
*,
|
|
model: str,
|
|
src: types.EmbeddingsBatchJobSourceOrDict,
|
|
config: Optional[types.CreateEmbeddingsBatchJobConfigOrDict] = None,
|
|
) -> types.BatchJob:
|
|
"""**Experimental** Creates an embedding batch job.
|
|
|
|
Args:
|
|
model (str): The model to use for the batch job.
|
|
src: Gemini Developer API supports List of inlined_request, or file name.
|
|
Example: "files/file_name".
|
|
config (CreateBatchJobConfig): Optional configuration for the batch job.
|
|
|
|
Returns:
|
|
A BatchJob object that contains details about the batch job.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
batch_job = client.batches.create_embeddings(
|
|
model="text-embedding-004",
|
|
src="files/my_embedding_input",
|
|
)
|
|
print(batch_job.state)
|
|
"""
|
|
import warnings
|
|
|
|
warnings.warn(
|
|
'batches.create_embeddings() is experimental and may change without'
|
|
' notice.',
|
|
category=_common.ExperimentalWarning,
|
|
stacklevel=2, # This is crucial!
|
|
)
|
|
src = t.t_embedding_batch_job_source(self._api_client, src)
|
|
|
|
# Convert all dicts to Pydantic objects.
|
|
parameter_model = types._CreateEmbeddingsBatchJobParameters(
|
|
model=model,
|
|
src=src,
|
|
config=config,
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
raise ValueError('Vertex AI does not support batches.create_embeddings.')
|
|
else:
|
|
return self._create_embeddings(model=model, src=src, config=config)
|
|
|
|
def list(
|
|
self, *, config: Optional[types.ListBatchJobsConfigOrDict] = None
|
|
) -> Pager[types.BatchJob]:
|
|
"""Lists batch jobs.
|
|
|
|
Args:
|
|
config (ListBatchJobsConfig): Optional configuration for the list request.
|
|
|
|
Returns:
|
|
A Pager object that contains one page of batch jobs. When iterating over
|
|
the pager, it automatically fetches the next page if there are more.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
config = {'page_size': 10}
|
|
for batch_job in client.batches.list(config):
|
|
print(batch_job.name)
|
|
"""
|
|
|
|
list_request = self._list
|
|
return Pager(
|
|
'batch_jobs',
|
|
list_request,
|
|
self._list(config=config),
|
|
config,
|
|
)
|
|
|
|
|
|
class AsyncBatches(_api_module.BaseModule):
|
|
|
|
async def _create(
|
|
self,
|
|
*,
|
|
model: Optional[str] = None,
|
|
src: Union[types.BatchJobSourceUnion, types.BatchJobSourceUnionDict],
|
|
config: Optional[types.CreateBatchJobConfigOrDict] = None,
|
|
) -> types.BatchJob:
|
|
parameter_model = types._CreateBatchJobParameters(
|
|
model=model,
|
|
src=src,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _CreateBatchJobParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = 'batchPredictionJobs'.format_map(request_url_dict)
|
|
else:
|
|
path = 'batchPredictionJobs'
|
|
else:
|
|
request_dict = _CreateBatchJobParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:batchGenerateContent'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:batchGenerateContent'
|
|
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 = await self._api_client.async_request(
|
|
'post', path, request_dict, http_options
|
|
)
|
|
|
|
response_dict = {} if not response.body else json.loads(response.body)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _BatchJob_from_vertex(response_dict)
|
|
|
|
if not self._api_client.vertexai:
|
|
response_dict = _BatchJob_from_mldev(response_dict)
|
|
|
|
return_value = types.BatchJob._from_response(
|
|
response=response_dict,
|
|
kwargs={
|
|
'config': {
|
|
'response_schema': getattr(
|
|
parameter_model.config, 'response_schema', None
|
|
),
|
|
'response_json_schema': getattr(
|
|
parameter_model.config, 'response_json_schema', None
|
|
),
|
|
'include_all_fields': getattr(
|
|
parameter_model.config, 'include_all_fields', None
|
|
),
|
|
}
|
|
}
|
|
if getattr(parameter_model, 'config', None)
|
|
else {},
|
|
)
|
|
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
async def _create_embeddings(
|
|
self,
|
|
*,
|
|
model: Optional[str] = None,
|
|
src: types.EmbeddingsBatchJobSourceOrDict,
|
|
config: Optional[types.CreateEmbeddingsBatchJobConfigOrDict] = None,
|
|
) -> types.BatchJob:
|
|
parameter_model = types._CreateEmbeddingsBatchJobParameters(
|
|
model=model,
|
|
src=src,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
if self._api_client.vertexai:
|
|
raise ValueError(
|
|
'This method is only supported in the Gemini Developer client.'
|
|
)
|
|
else:
|
|
request_dict = _CreateEmbeddingsBatchJobParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = '{model}:asyncBatchEmbedContent'.format_map(request_url_dict)
|
|
else:
|
|
path = '{model}:asyncBatchEmbedContent'
|
|
|
|
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 = await self._api_client.async_request(
|
|
'post', path, request_dict, http_options
|
|
)
|
|
|
|
response_dict = {} if not response.body else json.loads(response.body)
|
|
|
|
if not self._api_client.vertexai:
|
|
response_dict = _BatchJob_from_mldev(response_dict)
|
|
|
|
return_value = types.BatchJob._from_response(
|
|
response=response_dict,
|
|
kwargs={
|
|
'config': {
|
|
'response_schema': getattr(
|
|
parameter_model.config, 'response_schema', None
|
|
),
|
|
'response_json_schema': getattr(
|
|
parameter_model.config, 'response_json_schema', None
|
|
),
|
|
'include_all_fields': getattr(
|
|
parameter_model.config, 'include_all_fields', None
|
|
),
|
|
}
|
|
}
|
|
if getattr(parameter_model, 'config', None)
|
|
else {},
|
|
)
|
|
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
async def get(
|
|
self, *, name: str, config: Optional[types.GetBatchJobConfigOrDict] = None
|
|
) -> types.BatchJob:
|
|
"""Gets a batch job.
|
|
|
|
Args:
|
|
name (str): A fully-qualified BatchJob resource name or ID.
|
|
Example: "projects/.../locations/.../batchPredictionJobs/456" or "456"
|
|
when project and location are initialized in the Vertex AI client. Or
|
|
"batches/abc" using the Gemini Developer AI client.
|
|
|
|
Returns:
|
|
A BatchJob object that contains details about the batch job.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
batch_job = await client.aio.batches.get(name='123456789')
|
|
print(f"Batch job: {batch_job.name}, state {batch_job.state}")
|
|
"""
|
|
|
|
parameter_model = types._GetBatchJobParameters(
|
|
name=name,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _GetBatchJobParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = 'batchPredictionJobs/{name}'.format_map(request_url_dict)
|
|
else:
|
|
path = 'batchPredictionJobs/{name}'
|
|
else:
|
|
request_dict = _GetBatchJobParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = 'batches/{name}'.format_map(request_url_dict)
|
|
else:
|
|
path = 'batches/{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 = await self._api_client.async_request(
|
|
'get', path, request_dict, http_options
|
|
)
|
|
|
|
response_dict = {} if not response.body else json.loads(response.body)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _BatchJob_from_vertex(response_dict)
|
|
|
|
if not self._api_client.vertexai:
|
|
response_dict = _BatchJob_from_mldev(response_dict)
|
|
|
|
return_value = types.BatchJob._from_response(
|
|
response=response_dict,
|
|
kwargs={
|
|
'config': {
|
|
'response_schema': getattr(
|
|
parameter_model.config, 'response_schema', None
|
|
),
|
|
'response_json_schema': getattr(
|
|
parameter_model.config, 'response_json_schema', None
|
|
),
|
|
'include_all_fields': getattr(
|
|
parameter_model.config, 'include_all_fields', None
|
|
),
|
|
}
|
|
}
|
|
if getattr(parameter_model, 'config', None)
|
|
else {},
|
|
)
|
|
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
async def cancel(
|
|
self,
|
|
*,
|
|
name: str,
|
|
config: Optional[types.CancelBatchJobConfigOrDict] = None,
|
|
) -> None:
|
|
"""Cancels a batch job.
|
|
|
|
Only available for batch jobs that are running or pending.
|
|
|
|
Args:
|
|
name (str): A fully-qualified BatchJob resource name or ID.
|
|
Example: "projects/.../locations/.../batchPredictionJobs/456" or "456"
|
|
when project and location are initialized in the Vertex AI client. Or
|
|
"batches/abc" using the Gemini Developer AI client.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
await client.aio.batches.cancel(name='123456789')
|
|
"""
|
|
|
|
parameter_model = types._CancelBatchJobParameters(
|
|
name=name,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _CancelBatchJobParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = 'batchPredictionJobs/{name}:cancel'.format_map(request_url_dict)
|
|
else:
|
|
path = 'batchPredictionJobs/{name}:cancel'
|
|
else:
|
|
request_dict = _CancelBatchJobParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = 'batches/{name}:cancel'.format_map(request_url_dict)
|
|
else:
|
|
path = 'batches/{name}:cancel'
|
|
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)
|
|
|
|
await self._api_client.async_request(
|
|
'post', path, request_dict, http_options
|
|
)
|
|
|
|
async def _list(
|
|
self, *, config: Optional[types.ListBatchJobsConfigOrDict] = None
|
|
) -> types.ListBatchJobsResponse:
|
|
parameter_model = types._ListBatchJobsParameters(
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _ListBatchJobsParameters_to_vertex(parameter_model)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = 'batchPredictionJobs'.format_map(request_url_dict)
|
|
else:
|
|
path = 'batchPredictionJobs'
|
|
else:
|
|
request_dict = _ListBatchJobsParameters_to_mldev(parameter_model)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = 'batches'.format_map(request_url_dict)
|
|
else:
|
|
path = 'batches'
|
|
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 = await self._api_client.async_request(
|
|
'get', path, request_dict, http_options
|
|
)
|
|
|
|
response_dict = {} if not response.body else json.loads(response.body)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _ListBatchJobsResponse_from_vertex(response_dict)
|
|
|
|
if not self._api_client.vertexai:
|
|
response_dict = _ListBatchJobsResponse_from_mldev(response_dict)
|
|
|
|
return_value = types.ListBatchJobsResponse._from_response(
|
|
response=response_dict,
|
|
kwargs={
|
|
'config': {
|
|
'response_schema': getattr(
|
|
parameter_model.config, 'response_schema', None
|
|
),
|
|
'response_json_schema': getattr(
|
|
parameter_model.config, 'response_json_schema', None
|
|
),
|
|
'include_all_fields': getattr(
|
|
parameter_model.config, 'include_all_fields', None
|
|
),
|
|
}
|
|
}
|
|
if getattr(parameter_model, 'config', None)
|
|
else {},
|
|
)
|
|
return_value.sdk_http_response = types.HttpResponse(
|
|
headers=response.headers
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
async def delete(
|
|
self,
|
|
*,
|
|
name: str,
|
|
config: Optional[types.DeleteBatchJobConfigOrDict] = None,
|
|
) -> types.DeleteResourceJob:
|
|
"""Deletes a batch job.
|
|
|
|
Args:
|
|
name (str): A fully-qualified BatchJob resource name or ID.
|
|
Example: "projects/.../locations/.../batchPredictionJobs/456" or "456"
|
|
when project and location are initialized in the client.
|
|
|
|
Returns:
|
|
A DeleteResourceJob object that shows the status of the deletion.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
await client.aio.batches.delete(name='123456789')
|
|
"""
|
|
|
|
parameter_model = types._DeleteBatchJobParameters(
|
|
name=name,
|
|
config=config,
|
|
)
|
|
|
|
request_url_dict: Optional[dict[str, str]]
|
|
|
|
if self._api_client.vertexai:
|
|
request_dict = _DeleteBatchJobParameters_to_vertex(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = 'batchPredictionJobs/{name}'.format_map(request_url_dict)
|
|
else:
|
|
path = 'batchPredictionJobs/{name}'
|
|
else:
|
|
request_dict = _DeleteBatchJobParameters_to_mldev(
|
|
self._api_client, parameter_model
|
|
)
|
|
request_url_dict = request_dict.get('_url')
|
|
if request_url_dict:
|
|
path = 'batches/{name}'.format_map(request_url_dict)
|
|
else:
|
|
path = 'batches/{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 = await self._api_client.async_request(
|
|
'delete', path, request_dict, http_options
|
|
)
|
|
|
|
response_dict = {} if not response.body else json.loads(response.body)
|
|
|
|
if self._api_client.vertexai:
|
|
response_dict = _DeleteResourceJob_from_vertex(response_dict)
|
|
|
|
if not self._api_client.vertexai:
|
|
response_dict = _DeleteResourceJob_from_mldev(response_dict)
|
|
|
|
return_value = types.DeleteResourceJob._from_response(
|
|
response=response_dict,
|
|
kwargs={
|
|
'config': {
|
|
'response_schema': getattr(
|
|
parameter_model.config, 'response_schema', None
|
|
),
|
|
'response_json_schema': getattr(
|
|
parameter_model.config, 'response_json_schema', None
|
|
),
|
|
'include_all_fields': getattr(
|
|
parameter_model.config, 'include_all_fields', None
|
|
),
|
|
}
|
|
}
|
|
if getattr(parameter_model, 'config', None)
|
|
else {},
|
|
)
|
|
return_value.sdk_http_response = types.HttpResponse(
|
|
headers=response.headers
|
|
)
|
|
self._api_client._verify_response(return_value)
|
|
return return_value
|
|
|
|
async def create(
|
|
self,
|
|
*,
|
|
model: str,
|
|
src: types.BatchJobSourceUnionDict,
|
|
config: Optional[types.CreateBatchJobConfigOrDict] = None,
|
|
) -> types.BatchJob:
|
|
"""Creates a batch job asynchronously.
|
|
|
|
Args:
|
|
model (str): The model to use for the batch job.
|
|
src: The source of the batch job. Currently Vertex AI supports GCS URI(-s)
|
|
or BigQuery URI. Example: "gs://path/to/input/data" or
|
|
"bq://projectId.bqDatasetId.bqTableId". Gemini Develop API supports List
|
|
of inlined_request, or file name. Example: "files/file_name".
|
|
config (CreateBatchJobConfig): Optional configuration for the batch job.
|
|
|
|
Returns:
|
|
A BatchJob object that contains details about the batch job.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
batch_job = await client.aio.batches.create(
|
|
model="gemini-2.0-flash-001",
|
|
src="gs://path/to/input/data",
|
|
)
|
|
"""
|
|
src = t.t_batch_job_source(self._api_client, src)
|
|
|
|
# Convert all dicts to Pydantic objects.
|
|
parameter_model = types._CreateBatchJobParameters(
|
|
model=model,
|
|
src=src,
|
|
config=config,
|
|
)
|
|
|
|
if self._api_client.vertexai:
|
|
config = _extra_utils.format_destination(src, parameter_model.config)
|
|
return await self._create(model=model, src=src, config=config)
|
|
else:
|
|
return await self._create(model=model, src=src, config=config)
|
|
|
|
async def create_embeddings(
|
|
self,
|
|
*,
|
|
model: str,
|
|
src: types.EmbeddingsBatchJobSourceOrDict,
|
|
config: Optional[types.CreateEmbeddingsBatchJobConfigOrDict] = None,
|
|
) -> types.BatchJob:
|
|
"""**Experimental** Creates an asynchronously embedding batch job.
|
|
|
|
Args:
|
|
model (str): The model to use for the batch job.
|
|
src: Gemini Developer API supports inlined_requests, or file name.
|
|
Example: "files/file_name".
|
|
config (CreateBatchJobConfig): Optional configuration for the batch job.
|
|
|
|
Returns:
|
|
A BatchJob object that contains details about the batch job.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
|
|
batch_job = await client.aio.batches.create_embeddings(
|
|
model="text-embedding-004",
|
|
src="files/my_embedding_input",
|
|
)
|
|
print(batch_job.state)
|
|
"""
|
|
import warnings
|
|
|
|
warnings.warn(
|
|
'batches.create_embeddings() is experimental and may change without'
|
|
' notice.',
|
|
category=_common.ExperimentalWarning,
|
|
stacklevel=2, # This is crucial!
|
|
)
|
|
src = t.t_embedding_batch_job_source(self._api_client, src)
|
|
|
|
# Convert all dicts to Pydantic objects.
|
|
parameter_model = types._CreateEmbeddingsBatchJobParameters(
|
|
model=model,
|
|
src=src,
|
|
config=config,
|
|
)
|
|
|
|
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
|
|
|
|
if self._api_client.vertexai:
|
|
raise ValueError('Vertex AI does not support batches.create_embeddings.')
|
|
else:
|
|
return await self._create_embeddings(model=model, src=src, config=config)
|
|
|
|
async def list(
|
|
self, *, config: Optional[types.ListBatchJobsConfigOrDict] = None
|
|
) -> AsyncPager[types.BatchJob]:
|
|
"""Lists batch jobs asynchronously.
|
|
|
|
Args:
|
|
config (ListBatchJobsConfig): Optional configuration for the list request.
|
|
|
|
Returns:
|
|
A Pager object that contains one page of batch jobs. When iterating over
|
|
the pager, it automatically fetches the next page if there are more.
|
|
|
|
Usage:
|
|
|
|
.. code-block:: python
|
|
async for batch_job in await client.aio.batches.list():
|
|
print(batch_job.name)
|
|
"""
|
|
|
|
list_request = self._list
|
|
return AsyncPager(
|
|
'batch_jobs',
|
|
list_request,
|
|
await self._list(config=config),
|
|
config,
|
|
)
|