From a3c46f510a158218d21af44fcdc0fdff9e04d0f2 Mon Sep 17 00:00:00 2001 From: Matthew Tang Date: Thu, 14 Aug 2025 15:40:30 -0700 Subject: [PATCH 1/9] feat: Support Imagen image segmentation on Vertex PiperOrigin-RevId: 795227899 --- google/genai/models.py | 340 ++++++++++++++++++ .../tests/data/segmentation_scribble.jpg | Bin 0 -> 7880 bytes .../genai/tests/data/skateboard_stop_sign.jpg | Bin 0 -> 351072 bytes .../genai/tests/models/test_segment_image.py | 147 ++++++++ google/genai/types.py | 240 +++++++++++++ 5 files changed, 727 insertions(+) create mode 100644 google/genai/tests/data/segmentation_scribble.jpg create mode 100644 google/genai/tests/data/skateboard_stop_sign.jpg create mode 100644 google/genai/tests/models/test_segment_image.py diff --git a/google/genai/models.py b/google/genai/models.py index ee5888257..e414e9d9b 100644 --- a/google/genai/models.py +++ b/google/genai/models.py @@ -3025,6 +3025,120 @@ def _RecontextImageParameters_to_vertex( return to_object +def _ScribbleImage_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, ['image']) is not None: + setv( + to_object, + ['image'], + _Image_to_vertex(getv(from_object, ['image']), to_object), + ) + + return to_object + + +def _SegmentImageSource_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, ['prompt']) is not None: + setv( + parent_object, ['instances[0]', 'prompt'], getv(from_object, ['prompt']) + ) + + if getv(from_object, ['image']) is not None: + setv( + parent_object, + ['instances[0]', 'image'], + _Image_to_vertex(getv(from_object, ['image']), to_object), + ) + + if getv(from_object, ['scribble_image']) is not None: + setv( + parent_object, + ['instances[0]', 'scribble'], + _ScribbleImage_to_vertex( + getv(from_object, ['scribble_image']), to_object + ), + ) + + return to_object + + +def _SegmentImageConfig_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, ['mode']) is not None: + setv(parent_object, ['parameters', 'mode'], getv(from_object, ['mode'])) + + if getv(from_object, ['max_predictions']) is not None: + setv( + parent_object, + ['parameters', 'maxPredictions'], + getv(from_object, ['max_predictions']), + ) + + if getv(from_object, ['confidence_threshold']) is not None: + setv( + parent_object, + ['parameters', 'confidenceThreshold'], + getv(from_object, ['confidence_threshold']), + ) + + if getv(from_object, ['mask_dilation']) is not None: + setv( + parent_object, + ['parameters', 'maskDilation'], + getv(from_object, ['mask_dilation']), + ) + + if getv(from_object, ['binary_color_threshold']) is not None: + setv( + parent_object, + ['parameters', 'binaryColorThreshold'], + getv(from_object, ['binary_color_threshold']), + ) + + return to_object + + +def _SegmentImageParameters_to_vertex( + api_client: BaseApiClient, + from_object: Union[dict[str, Any], object], + parent_object: Optional[dict[str, Any]] = None, +) -> dict[str, Any]: + to_object: dict[str, Any] = {} + if getv(from_object, ['model']) is not None: + setv( + to_object, + ['_url', 'model'], + t.t_model(api_client, getv(from_object, ['model'])), + ) + + if getv(from_object, ['source']) is not None: + setv( + to_object, + ['config'], + _SegmentImageSource_to_vertex(getv(from_object, ['source']), to_object), + ) + + if getv(from_object, ['config']) is not None: + setv( + to_object, + ['config'], + _SegmentImageConfig_to_vertex(getv(from_object, ['config']), to_object), + ) + + return to_object + + def _GetModelParameters_to_vertex( api_client: BaseApiClient, from_object: Union[dict[str, Any], object], @@ -4618,6 +4732,63 @@ def _RecontextImageResponse_from_vertex( return to_object +def _EntityLabel_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, ['label']) is not None: + setv(to_object, ['label'], getv(from_object, ['label'])) + + if getv(from_object, ['score']) is not None: + setv(to_object, ['score'], getv(from_object, ['score'])) + + return to_object + + +def _GeneratedImageMask_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, ['_self']) is not None: + setv( + to_object, + ['mask'], + _Image_from_vertex(getv(from_object, ['_self']), to_object), + ) + + if getv(from_object, ['labels']) is not None: + setv( + to_object, + ['labels'], + [ + _EntityLabel_from_vertex(item, to_object) + for item in getv(from_object, ['labels']) + ], + ) + + return to_object + + +def _SegmentImageResponse_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, ['predictions']) is not None: + setv( + to_object, + ['generated_masks'], + [ + _GeneratedImageMask_from_vertex(item, to_object) + for item in getv(from_object, ['predictions']) + ], + ) + + return to_object + + def _Endpoint_from_vertex( from_object: Union[dict[str, Any], object], parent_object: Optional[dict[str, Any]] = None, @@ -5511,6 +5682,89 @@ def recontext_image( self._api_client._verify_response(return_value) return return_value + def segment_image( + self, + *, + model: str, + source: types.SegmentImageSourceOrDict, + config: Optional[types.SegmentImageConfigOrDict] = None, + ) -> types.SegmentImageResponse: + """Segments an image, creating a mask of a specified area. + + Args: + model (str): The model to use. + source (SegmentImageSource): An object containing the source inputs + (prompt, image, scribble_image) for image segmentation. The prompt is + required for prompt mode and semantic mode, disallowed for other modes. + scribble_image is required for the interactive mode, disallowed for + other modes. + config (SegmentImageConfig): Configuration for segmentation. + + Usage: + + ``` + response = client.models.segment_image( + model="image-segmentation-001", + source=types.SegmentImageSource( + image=types.Image.from_file(IMAGE_FILE_PATH), + ), + ) + + mask_image = response.generated_masks[0].mask + ``` + """ + + parameter_model = types._SegmentImageParameters( + model=model, + source=source, + config=config, + ) + + request_url_dict: Optional[dict[str, str]] + if not self._api_client.vertexai: + raise ValueError('This method is only supported in the Vertex AI client.') + else: + request_dict = _SegmentImageParameters_to_vertex( + self._api_client, parameter_model + ) + request_url_dict = request_dict.get('_url') + if request_url_dict: + path = '{model}:predict'.format_map(request_url_dict) + else: + path = '{model}:predict' + + query_params = request_dict.get('_query') + if query_params: + path = f'{path}?{urlencode(query_params)}' + # TODO: remove the hack that pops config. + request_dict.pop('config', None) + + http_options: Optional[types.HttpOptions] = None + if ( + parameter_model.config is not None + and parameter_model.config.http_options is not None + ): + http_options = parameter_model.config.http_options + + request_dict = _common.convert_to_dict(request_dict) + request_dict = _common.encode_unserializable_types(request_dict) + + response = 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 = _SegmentImageResponse_from_vertex(response_dict) + + return_value = types.SegmentImageResponse._from_response( + response=response_dict, kwargs=parameter_model.model_dump() + ) + + self._api_client._verify_response(return_value) + return return_value + def get( self, *, model: str, config: Optional[types.GetModelConfigOrDict] = None ) -> types.Model: @@ -7240,6 +7494,92 @@ async def recontext_image( self._api_client._verify_response(return_value) return return_value + async def segment_image( + self, + *, + model: str, + source: types.SegmentImageSourceOrDict, + config: Optional[types.SegmentImageConfigOrDict] = None, + ) -> types.SegmentImageResponse: + """Segments an image, creating a mask of a specified area. + + Args: + model (str): The model to use. + source (SegmentImageSource): An object containing the source inputs + (prompt, image, scribble_image) for image segmentation. The prompt is + required for prompt mode and semantic mode, disallowed for other modes. + scribble_image is required for the interactive mode, disallowed for + other modes. + config (SegmentImageConfig): Configuration for segmentation. + + Usage: + + ``` + response = client.models.segment_image( + model="image-segmentation-001", + source=types.SegmentImageSource( + image=types.Image.from_file(IMAGE_FILE_PATH), + ), + config=types.SegmentImageConfig( + mode=types.SegmentMode.foreground, + ), + ) + + mask_image = response.generated_masks[0].mask + ``` + """ + + parameter_model = types._SegmentImageParameters( + model=model, + source=source, + config=config, + ) + + request_url_dict: Optional[dict[str, str]] + if not self._api_client.vertexai: + raise ValueError('This method is only supported in the Vertex AI client.') + else: + request_dict = _SegmentImageParameters_to_vertex( + self._api_client, parameter_model + ) + request_url_dict = request_dict.get('_url') + if request_url_dict: + path = '{model}:predict'.format_map(request_url_dict) + else: + path = '{model}:predict' + + query_params = request_dict.get('_query') + if query_params: + path = f'{path}?{urlencode(query_params)}' + # TODO: remove the hack that pops config. + request_dict.pop('config', None) + + http_options: Optional[types.HttpOptions] = None + if ( + parameter_model.config is not None + and parameter_model.config.http_options is not None + ): + http_options = parameter_model.config.http_options + + request_dict = _common.convert_to_dict(request_dict) + request_dict = _common.encode_unserializable_types(request_dict) + + response = 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 = _SegmentImageResponse_from_vertex(response_dict) + + return_value = types.SegmentImageResponse._from_response( + response=response_dict, 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Submodules +
+async segment_image(*, model, source, config=None)
+

Segments an image, creating a mask of a specified area.

+
+
Return type:
+

SegmentImageResponse

+
+
Parameters:
+
    +

  • model (str) – The model to use.

    • +

  • source (SegmentImageSource) – An object containing the source inputs +(prompt, image, scribble_image) for image segmentation. The prompt is +required for prompt mode and semantic mode, disallowed for other modes. +scribble_image is required for the interactive mode, disallowed for +other modes.

    • +

  • config (SegmentImageConfig) – Configuration for segmentation.

    • +
+
+
+

Usage:

+
+

``` +response = client.models.segment_image(

+
+

model=”image-segmentation-001”, +source=types.SegmentImageSource(

+
+

image=types.Image.from_file(IMAGE_FILE_PATH),

+
+

), +config=types.SegmentImageConfig(

+
+

mode=types.SegmentMode.foreground,

+
+

),

+
+

)

+

mask_image = response.generated_masks[0].mask +```

+
+
+
async update(*, model, config=None)
@@ -2691,7 +2734,7 @@

Submodules``` +

``` operation = client.models.generate_videos(

model=”veo-2.0-generate-001”, @@ -2704,7 +2747,7 @@

Submodules```

+```

@@ -2780,7 +2823,7 @@

Submodules``` +

``` product_recontext_response = client.models.recontext_image(

model=”imagen-product-recontext-preview-06-30”, @@ -2815,7 +2858,45 @@

Submodules```

+```

+

+ + +
+
+segment_image(*, model, source, config=None)
+

Segments an image, creating a mask of a specified area.

+
+
Return type:
+

SegmentImageResponse

+
+
Parameters:
+
    +

  • model (str) – The model to use.

    • +

  • source (SegmentImageSource) – An object containing the source inputs +(prompt, image, scribble_image) for image segmentation. The prompt is +required for prompt mode and semantic mode, disallowed for other modes. +scribble_image is required for the interactive mode, disallowed for +other modes.

    • +

  • config (SegmentImageConfig) – Configuration for segmentation.

    • +
+
+
+

Usage:

+
+

``` +response = client.models.segment_image(

+
+

model=”image-segmentation-001”, +source=types.SegmentImageSource(

+
+

image=types.Image.from_file(IMAGE_FILE_PATH),

+
+

),

+
+

)

+

mask_image = response.generated_masks[0].mask +```

@@ -4999,6 +5080,143 @@

Submodules +
+pydantic model genai.types.AutoraterConfig
+

Bases: BaseModel

+

Autorater config used for evaluation.

+

Create a new model by parsing and validating input data from keyword arguments.

+

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be +validated to form a valid model.

+

self is explicitly positional-only to allow self as a field name.

+

+Show JSON schema
{
+   "title": "AutoraterConfig",
+   "description": "Autorater config used for evaluation.",
+   "type": "object",
+   "properties": {
+      "samplingCount": {
+         "anyOf": [
+            {
+               "type": "integer"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "Number of samples for each instance in the dataset.\n  If not specified, the default is 4. Minimum value is 1, maximum value\n  is 32.",
+         "title": "Samplingcount"
+      },
+      "flipEnabled": {
+         "anyOf": [
+            {
+               "type": "boolean"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "Optional. Default is true. Whether to flip the candidate and baseline\n  responses. This is only applicable to the pairwise metric. If enabled, also\n  provide PairwiseMetricSpec.candidate_response_field_name and\n  PairwiseMetricSpec.baseline_response_field_name. When rendering\n  PairwiseMetricSpec.metric_prompt_template, the candidate and baseline\n  fields will be flipped for half of the samples to reduce bias.",
+         "title": "Flipenabled"
+      },
+      "autoraterModel": {
+         "anyOf": [
+            {
+               "type": "string"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "The fully qualified name of the publisher model or tuned autorater\n  endpoint to use.\n\n  Publisher model format:\n  `projects/{project}/locations/{location}/publishers/*/models/*`\n\n  Tuned model endpoint format:\n  `projects/{project}/locations/{location}/endpoints/{endpoint}`",
+         "title": "Autoratermodel"
+      }
+   },
+   "additionalProperties": false
+}
+
+
+

+
Fields:
+
+
+
+
+
+field autorater_model: Optional[str] = None (alias 'autoraterModel')
+

The fully qualified name of the publisher model or tuned autorater +endpoint to use.

+

Publisher model format: +projects/{project}/locations/{location}/publishers/*/models/*

+

Tuned model endpoint format: +projects/{project}/locations/{location}/endpoints/{endpoint}

+
+ +
+
+field flip_enabled: Optional[bool] = None (alias 'flipEnabled')
+

Optional. Default is true. Whether to flip the candidate and baseline +responses. This is only applicable to the pairwise metric. If enabled, also +provide PairwiseMetricSpec.candidate_response_field_name and +PairwiseMetricSpec.baseline_response_field_name. When rendering +PairwiseMetricSpec.metric_prompt_template, the candidate and baseline +fields will be flipped for half of the samples to reduce bias.

+
+ +
+
+field sampling_count: Optional[int] = None (alias 'samplingCount')
+

Number of samples for each instance in the dataset. +If not specified, the default is 4. Minimum value is 1, maximum value +is 32.

+
+ +
+ +
+
+class genai.types.AutoraterConfigDict
+

Bases: TypedDict

+

Autorater config used for evaluation.

+
+
+autorater_model: Optional[str]
+

The fully qualified name of the publisher model or tuned autorater +endpoint to use.

+

Publisher model format: +projects/{project}/locations/{location}/publishers/*/models/*

+

Tuned model endpoint format: +projects/{project}/locations/{location}/endpoints/{endpoint}

+
+ +
+
+flip_enabled: Optional[bool]
+

Optional. Default is true. Whether to flip the candidate and baseline +responses. This is only applicable to the pairwise metric. If enabled, also +provide PairwiseMetricSpec.candidate_response_field_name and +PairwiseMetricSpec.baseline_response_field_name. When rendering +PairwiseMetricSpec.metric_prompt_template, the candidate and baseline +fields will be flipped for half of the samples to reduce bias.

+
+ +
+
+sampling_count: Optional[int]
+

Number of samples for each instance in the dataset. +If not specified, the default is 4. Minimum value is 1, maximum value +is 32.

+
+ +
+
pydantic model genai.types.BatchJob
@@ -6098,10 +6316,36 @@

Submodules "EnterpriseWebSearch": { "additionalProperties": false, "description": "Tool to search public web data, powered by Vertex AI Search and Sec4 compliance.", - "properties": {}, + "properties": { + "excludeDomains": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. List of domains to be excluded from the search results. The default limit is 2000 domains.", + "title": "Excludedomains" + } + }, "title": "EnterpriseWebSearch", "type": "object" }, + "Environment": { + "description": "The environment being operated.", + "enum": [ + "ENVIRONMENT_UNSPECIFIED", + "ENVIRONMENT_BROWSER" + ], + "title": "Environment", + "type": "string" + }, "ExecutableCode": { "additionalProperties": false, "description": "Code generated by the model that is meant to be executed, and the result returned to the model.\n\nGenerated when using the [CodeExecution] tool, in which the code will be\nautomatically executed, and a corresponding [CodeExecutionResult] will also be\ngenerated.", @@ -7769,6 +8013,22 @@

Submodules ], "default": null, "description": "Optional. Filter search results to a specific time range.\n If customers set a start time, they must set an end time (and vice versa).\n " + }, + "excludeDomains": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. List of domains to be excluded from the search results.\n The default limit is 2000 domains.", + "title": "Excludedomains" } }, "title": "GoogleSearch", @@ -8117,6 +8377,19 @@

Submodules "additionalProperties": false, "description": "Chunk from context retrieved by the retrieval tools.", "properties": { + "documentName": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Output only. The full document name for the referenced Vertex AI Search document.", + "title": "Documentname" + }, "ragChunk": { "anyOf": [ { @@ -10487,6 +10760,26 @@

Submodules "title": "ToolCodeExecution", "type": "object" }, + "ToolComputerUse": { + "additionalProperties": false, + "description": "Tool to support computer use.", + "properties": { + "environment": { + "anyOf": [ + { + "$ref": "#/$defs/Environment" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Required. The environment being operated." + } + }, + "title": "ToolComputerUse", + "type": "object" + }, "ToolConfig": { "additionalProperties": false, "description": "Tool config.\n\nThis config is shared for all tools provided in the request.", @@ -11017,6 +11310,18 @@

Submodules "default": null, "description": "Optional. Tool to support URL context retrieval." }, + "computerUse": { + "anyOf": [ + { + "$ref": "#/$defs/ToolComputerUse" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. Tool to support the model interacting directly with the\n computer. If enabled, it automatically populates computer-use specific\n Function Declarations." + }, "codeExecution": { "anyOf": [ { @@ -12544,6 +12849,19 @@

Submodules "additionalProperties": false, "description": "Chunk from context retrieved by the retrieval tools.", "properties": { + "documentName": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Output only. The full document name for the referenced Vertex AI Search document.", + "title": "Documentname" + }, "ragChunk": { "anyOf": [ { @@ -14462,10 +14780,36 @@

Submodules "EnterpriseWebSearch": { "additionalProperties": false, "description": "Tool to search public web data, powered by Vertex AI Search and Sec4 compliance.", - "properties": {}, + "properties": { + "excludeDomains": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. List of domains to be excluded from the search results. The default limit is 2000 domains.", + "title": "Excludedomains" + } + }, "title": "EnterpriseWebSearch", "type": "object" }, + "Environment": { + "description": "The environment being operated.", + "enum": [ + "ENVIRONMENT_UNSPECIFIED", + "ENVIRONMENT_BROWSER" + ], + "title": "Environment", + "type": "string" + }, "ExecutableCode": { "additionalProperties": false, "description": "Code generated by the model that is meant to be executed, and the result returned to the model.\n\nGenerated when using the [CodeExecution] tool, in which the code will be\nautomatically executed, and a corresponding [CodeExecutionResult] will also be\ngenerated.", @@ -15766,6 +16110,22 @@

Submodules ], "default": null, "description": "Optional. Filter search results to a specific time range.\n If customers set a start time, they must set an end time (and vice versa).\n " + }, + "excludeDomains": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. List of domains to be excluded from the search results.\n The default limit is 2000 domains.", + "title": "Excludedomains" } }, "title": "GoogleSearch", @@ -17301,6 +17661,26 @@

Submodules "title": "ToolCodeExecution", "type": "object" }, + "ToolComputerUse": { + "additionalProperties": false, + "description": "Tool to support computer use.", + "properties": { + "environment": { + "anyOf": [ + { + "$ref": "#/$defs/Environment" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Required. The environment being operated." + } + }, + "title": "ToolComputerUse", + "type": "object" + }, "ToolConfig": { "additionalProperties": false, "description": "Tool config.\n\nThis config is shared for all tools provided in the request.", @@ -17752,6 +18132,18 @@

Submodules "default": null, "description": "Optional. Tool to support URL context retrieval." }, + "computerUse": { + "anyOf": [ + { + "$ref": "#/$defs/ToolComputerUse" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. Tool to support the model interacting directly with the\n computer. If enabled, it automatically populates computer-use specific\n Function Declarations." + }, "codeExecution": { "anyOf": [ { @@ -17925,6 +18317,67 @@

Submodules +
+pydantic model genai.types.BleuSpec
+

Bases: BaseModel

+

Spec for bleu metric.

+

Create a new model by parsing and validating input data from keyword arguments.

+

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be +validated to form a valid model.

+

self is explicitly positional-only to allow self as a field name.

+

+Show JSON schema
{
+   "title": "BleuSpec",
+   "description": "Spec for bleu metric.",
+   "type": "object",
+   "properties": {
+      "useEffectiveOrder": {
+         "anyOf": [
+            {
+               "type": "boolean"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "Optional. Whether to use_effective_order to compute bleu score.",
+         "title": "Useeffectiveorder"
+      }
+   },
+   "additionalProperties": false
+}
+
+
+

+
Fields:
+
+
+
+
+
+field use_effective_order: Optional[bool] = None (alias 'useEffectiveOrder')
+

Optional. Whether to use_effective_order to compute bleu score.

+
+ +

+ +
+
+class genai.types.BleuSpecDict
+

Bases: TypedDict

+

Spec for bleu metric.

+
+
+use_effective_order: Optional[bool]
+

Optional. Whether to use_effective_order to compute bleu score.

+
+ +
+
pydantic model genai.types.Blob
@@ -19743,6 +20196,19 @@

Submodules "additionalProperties": false, "description": "Chunk from context retrieved by the retrieval tools.", "properties": { + "documentName": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Output only. The full document name for the referenced Vertex AI Search document.", + "title": "Documentname" + }, "ragChunk": { "anyOf": [ { @@ -23882,10 +24348,36 @@

Submodules "EnterpriseWebSearch": { "additionalProperties": false, "description": "Tool to search public web data, powered by Vertex AI Search and Sec4 compliance.", - "properties": {}, + "properties": { + "excludeDomains": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. List of domains to be excluded from the search results. The default limit is 2000 domains.", + "title": "Excludedomains" + } + }, "title": "EnterpriseWebSearch", "type": "object" }, + "Environment": { + "description": "The environment being operated.", + "enum": [ + "ENVIRONMENT_UNSPECIFIED", + "ENVIRONMENT_BROWSER" + ], + "title": "Environment", + "type": "string" + }, "ExecutableCode": { "additionalProperties": false, "description": "Code generated by the model that is meant to be executed, and the result returned to the model.\n\nGenerated when using the [CodeExecution] tool, in which the code will be\nautomatically executed, and a corresponding [CodeExecutionResult] will also be\ngenerated.", @@ -25036,6 +25528,22 @@

Submodules ], "default": null, "description": "Optional. Filter search results to a specific time range.\n If customers set a start time, they must set an end time (and vice versa).\n " + }, + "excludeDomains": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. List of domains to be excluded from the search results.\n The default limit is 2000 domains.", + "title": "Excludedomains" } }, "title": "GoogleSearch", @@ -26308,6 +26816,18 @@

Submodules "default": null, "description": "Optional. Tool to support URL context retrieval." }, + "computerUse": { + "anyOf": [ + { + "$ref": "#/$defs/ToolComputerUse" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. Tool to support the model interacting directly with the\n computer. If enabled, it automatically populates computer-use specific\n Function Declarations." + }, "codeExecution": { "anyOf": [ { @@ -26331,6 +26851,26 @@

Submodules "title": "ToolCodeExecution", "type": "object" }, + "ToolComputerUse": { + "additionalProperties": false, + "description": "Tool to support computer use.", + "properties": { + "environment": { + "anyOf": [ + { + "$ref": "#/$defs/Environment" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Required. The environment being operated." + } + }, + "title": "ToolComputerUse", + "type": "object" + }, "Type": { "description": "Optional. The type of the data.", "enum": [ @@ -27576,10 +28116,36 @@

Submodules "EnterpriseWebSearch": { "additionalProperties": false, "description": "Tool to search public web data, powered by Vertex AI Search and Sec4 compliance.", - "properties": {}, + "properties": { + "excludeDomains": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. List of domains to be excluded from the search results. The default limit is 2000 domains.", + "title": "Excludedomains" + } + }, "title": "EnterpriseWebSearch", "type": "object" }, + "Environment": { + "description": "The environment being operated.", + "enum": [ + "ENVIRONMENT_UNSPECIFIED", + "ENVIRONMENT_BROWSER" + ], + "title": "Environment", + "type": "string" + }, "ExecutableCode": { "additionalProperties": false, "description": "Code generated by the model that is meant to be executed, and the result returned to the model.\n\nGenerated when using the [CodeExecution] tool, in which the code will be\nautomatically executed, and a corresponding [CodeExecutionResult] will also be\ngenerated.", @@ -28730,6 +29296,22 @@

Submodules ], "default": null, "description": "Optional. Filter search results to a specific time range.\n If customers set a start time, they must set an end time (and vice versa).\n " + }, + "excludeDomains": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. List of domains to be excluded from the search results.\n The default limit is 2000 domains.", + "title": "Excludedomains" } }, "title": "GoogleSearch", @@ -30431,6 +31013,26 @@

Submodules "title": "ToolCodeExecution", "type": "object" }, + "ToolComputerUse": { + "additionalProperties": false, + "description": "Tool to support computer use.", + "properties": { + "environment": { + "anyOf": [ + { + "$ref": "#/$defs/Environment" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Required. The environment being operated." + } + }, + "title": "ToolComputerUse", + "type": "object" + }, "TurnCoverage": { "description": "Options about which input is included in the user's turn.", "enum": [ @@ -30860,6 +31462,18 @@

Submodules "default": null, "description": "Optional. Tool to support URL context retrieval." }, + "computerUse": { + "anyOf": [ + { + "$ref": "#/$defs/ToolComputerUse" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. Tool to support the model interacting directly with the\n computer. If enabled, it automatically populates computer-use specific\n Function Declarations." + }, "codeExecution": { "anyOf": [ { @@ -31746,10 +32360,36 @@

Submodules "EnterpriseWebSearch": { "additionalProperties": false, "description": "Tool to search public web data, powered by Vertex AI Search and Sec4 compliance.", - "properties": {}, + "properties": { + "excludeDomains": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. List of domains to be excluded from the search results. The default limit is 2000 domains.", + "title": "Excludedomains" + } + }, "title": "EnterpriseWebSearch", "type": "object" }, + "Environment": { + "description": "The environment being operated.", + "enum": [ + "ENVIRONMENT_UNSPECIFIED", + "ENVIRONMENT_BROWSER" + ], + "title": "Environment", + "type": "string" + }, "ExecutableCode": { "additionalProperties": false, "description": "Code generated by the model that is meant to be executed, and the result returned to the model.\n\nGenerated when using the [CodeExecution] tool, in which the code will be\nautomatically executed, and a corresponding [CodeExecutionResult] will also be\ngenerated.", @@ -32900,6 +33540,22 @@

Submodules ], "default": null, "description": "Optional. Filter search results to a specific time range.\n If customers set a start time, they must set an end time (and vice versa).\n " + }, + "excludeDomains": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. List of domains to be excluded from the search results.\n The default limit is 2000 domains.", + "title": "Excludedomains" } }, "title": "GoogleSearch", @@ -34601,6 +35257,26 @@

Submodules "title": "ToolCodeExecution", "type": "object" }, + "ToolComputerUse": { + "additionalProperties": false, + "description": "Tool to support computer use.", + "properties": { + "environment": { + "anyOf": [ + { + "$ref": "#/$defs/Environment" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Required. The environment being operated." + } + }, + "title": "ToolComputerUse", + "type": "object" + }, "TurnCoverage": { "description": "Options about which input is included in the user's turn.", "enum": [ @@ -35030,6 +35706,18 @@

Submodules "default": null, "description": "Optional. Tool to support URL context retrieval." }, + "computerUse": { + "anyOf": [ + { + "$ref": "#/$defs/ToolComputerUse" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. Tool to support the model interacting directly with the\n computer. If enabled, it automatically populates computer-use specific\n Function Declarations." + }, "codeExecution": { "anyOf": [ { @@ -36549,6 +37237,19 @@

Submodules "additionalProperties": false, "description": "Chunk from context retrieved by the retrieval tools.", "properties": { + "documentName": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Output only. The full document name for the referenced Vertex AI Search document.", + "title": "Documentname" + }, "ragChunk": { "anyOf": [ { @@ -38645,10 +39346,36 @@

Submodules "EnterpriseWebSearch": { "additionalProperties": false, "description": "Tool to search public web data, powered by Vertex AI Search and Sec4 compliance.", - "properties": {}, + "properties": { + "excludeDomains": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. List of domains to be excluded from the search results. The default limit is 2000 domains.", + "title": "Excludedomains" + } + }, "title": "EnterpriseWebSearch", "type": "object" }, + "Environment": { + "description": "The environment being operated.", + "enum": [ + "ENVIRONMENT_UNSPECIFIED", + "ENVIRONMENT_BROWSER" + ], + "title": "Environment", + "type": "string" + }, "ExecutableCode": { "additionalProperties": false, "description": "Code generated by the model that is meant to be executed, and the result returned to the model.\n\nGenerated when using the [CodeExecution] tool, in which the code will be\nautomatically executed, and a corresponding [CodeExecutionResult] will also be\ngenerated.", @@ -39429,6 +40156,22 @@

Submodules ], "default": null, "description": "Optional. Filter search results to a specific time range.\n If customers set a start time, they must set an end time (and vice versa).\n " + }, + "excludeDomains": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. List of domains to be excluded from the search results.\n The default limit is 2000 domains.", + "title": "Excludedomains" } }, "title": "GoogleSearch", @@ -40603,6 +41346,18 @@

Submodules "default": null, "description": "Optional. Tool to support URL context retrieval." }, + "computerUse": { + "anyOf": [ + { + "$ref": "#/$defs/ToolComputerUse" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. Tool to support the model interacting directly with the\n computer. If enabled, it automatically populates computer-use specific\n Function Declarations." + }, "codeExecution": { "anyOf": [ { @@ -40626,6 +41381,26 @@

Submodules "title": "ToolCodeExecution", "type": "object" }, + "ToolComputerUse": { + "additionalProperties": false, + "description": "Tool to support computer use.", + "properties": { + "environment": { + "anyOf": [ + { + "$ref": "#/$defs/Environment" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Required. The environment being operated." + } + }, + "title": "ToolComputerUse", + "type": "object" + }, "ToolConfig": { "additionalProperties": false, "description": "Tool config.\n\nThis config is shared for all tools provided in the request.", @@ -41534,7 +42309,6 @@

Submodules Show JSON schema
{
    "title": "CreateTuningJobConfig",
-   "description": "Supervised fine-tuning job creation request - optional fields.",
    "type": "object",
    "properties": {
       "httpOptions": {
@@ -41626,6 +42400,19 @@ 
Submodules         "description": "If set to true, disable intermediate checkpoints for SFT and only the last checkpoint will be exported. Otherwise, enable intermediate checkpoints for SFT.",
          "title": "Exportlastcheckpointonly"
       },
+      "preTunedModelCheckpointId": {
+         "anyOf": [
+            {
+               "type": "string"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "The optional checkpoint id of the pre-tuned model to use for tuning, if applicable.",
+         "title": "Pretunedmodelcheckpointid"
+      },
       "adapterSize": {
          "anyOf": [
             {
@@ -41663,6 +42450,10 @@ 
Submodules         "default": null,
          "description": "The learning rate hyperparameter for tuning. If not set, a default of 0.001 or 0.0002 will be calculated based on the number of training examples.",
          "title": "Learningrate"
+      },
+      "evaluationConfig": {
+         "default": null,
+         "title": "Evaluationconfig"
       }
    },
    "$defs": {
@@ -41928,10 +42719,12 @@ 
Submodulesbatch_size (int | None)

 
  • description (str | None)
    • 
 
  • epoch_count (int | None)
    • 
+
  • evaluation_config (genai.types.EvaluationConfig | None)
    • 
 
  • export_last_checkpoint_only (bool | None)
    • 
 
  • http_options (genai.types.HttpOptions | None)
    • 
 
  • learning_rate (float | None)
    • 
 
  • learning_rate_multiplier (float | None)
    • 
+
  • pre_tuned_model_checkpoint_id (str | None)
    • 
 
  • tuned_model_display_name (str | None)
    • 
 
  • validation_dataset (genai.types.TuningValidationDataset | None)
    • 
 
@@ -41961,6 +42754,12 @@ 
    Submodules
+
    
+field evaluation_config: Optional[EvaluationConfig] = None (alias 'evaluationConfig')
    
+
    Evaluation config for the tuning job.
    
+

    +
    field export_last_checkpoint_only: Optional[bool] = None (alias 'exportLastCheckpointOnly')
    @@ -41985,6 +42784,12 @@

    Submodules +
    +field pre_tuned_model_checkpoint_id: Optional[str] = None (alias 'preTunedModelCheckpointId')
    +

    The optional checkpoint id of the pre-tuned model to use for tuning, if applicable.

    +

    +
    field tuned_model_display_name: Optional[str] = None (alias 'tunedModelDisplayName')
    @@ -42028,6 +42833,12 @@

    Submodules +
    +evaluation_config: Optional[EvaluationConfigDict]
    +

    Evaluation config for the tuning job.

    +

    +
    export_last_checkpoint_only: Optional[bool]
    @@ -42052,6 +42863,12 @@

    Submodules +
    +pre_tuned_model_checkpoint_id: Optional[str]
    +

    The optional checkpoint id of the pre-tuned model to use for tuning, if applicable.

    +

    +
    tuned_model_display_name: Optional[str]
    @@ -42078,7 +42895,6 @@

    Submodules Show JSON schema
    {
    "title": "CreateTuningJobParameters",
-   "description": "Supervised fine-tuning job creation parameters - optional fields.",
    "type": "object",
    "properties": {
       "baseModel": {
@@ -42107,371 +42923,11 @@ 
    Submodules         "description": "Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file."
       },
       "config": {
-         "anyOf": [
-            {
-               "$ref": "#/$defs/CreateTuningJobConfig"
-            },
-            {
-               "type": "null"
-            }
-         ],
          "default": null,
-         "description": "Configuration for the tuning job."
+         "title": "Config"
       }
    },
    "$defs": {
-      "AdapterSize": {
-         "description": "Optional. Adapter size for tuning.",
-         "enum": [
-            "ADAPTER_SIZE_UNSPECIFIED",
-            "ADAPTER_SIZE_ONE",
-            "ADAPTER_SIZE_TWO",
-            "ADAPTER_SIZE_FOUR",
-            "ADAPTER_SIZE_EIGHT",
-            "ADAPTER_SIZE_SIXTEEN",
-            "ADAPTER_SIZE_THIRTY_TWO"
-         ],
-         "title": "AdapterSize",
-         "type": "string"
-      },
-      "CreateTuningJobConfig": {
-         "additionalProperties": false,
-         "description": "Supervised fine-tuning job creation request - optional fields.",
-         "properties": {
-            "httpOptions": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/HttpOptions"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Used to override HTTP request options."
-            },
-            "validationDataset": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/TuningValidationDataset"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file."
-            },
-            "tunedModelDisplayName": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The display name of the tuned Model. The name can be up to 128 characters long and can consist of any UTF-8 characters.",
-               "title": "Tunedmodeldisplayname"
-            },
-            "description": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The description of the TuningJob",
-               "title": "Description"
-            },
-            "epochCount": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Number of complete passes the model makes over the entire training dataset during training.",
-               "title": "Epochcount"
-            },
-            "learningRateMultiplier": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Multiplier for adjusting the default learning rate.",
-               "title": "Learningratemultiplier"
-            },
-            "exportLastCheckpointOnly": {
-               "anyOf": [
-                  {
-                     "type": "boolean"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "If set to true, disable intermediate checkpoints for SFT and only the last checkpoint will be exported. Otherwise, enable intermediate checkpoints for SFT.",
-               "title": "Exportlastcheckpointonly"
-            },
-            "adapterSize": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/AdapterSize"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Adapter size for tuning."
-            },
-            "batchSize": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The batch size hyperparameter for tuning. If not set, a default of 4 or 16 will be used based on the number of training examples.",
-               "title": "Batchsize"
-            },
-            "learningRate": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The learning rate hyperparameter for tuning. If not set, a default of 0.001 or 0.0002 will be calculated based on the number of training examples.",
-               "title": "Learningrate"
-            }
-         },
-         "title": "CreateTuningJobConfig",
-         "type": "object"
-      },
-      "HttpOptions": {
-         "additionalProperties": false,
-         "description": "HTTP options to be used in each of the requests.",
-         "properties": {
-            "baseUrl": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The base URL for the AI platform service endpoint.",
-               "title": "Baseurl"
-            },
-            "apiVersion": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Specifies the version of the API to use.",
-               "title": "Apiversion"
-            },
-            "headers": {
-               "anyOf": [
-                  {
-                     "additionalProperties": {
-                        "type": "string"
-                     },
-                     "type": "object"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Additional HTTP headers to be sent with the request.",
-               "title": "Headers"
-            },
-            "timeout": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Timeout for the request in milliseconds.",
-               "title": "Timeout"
-            },
-            "clientArgs": {
-               "anyOf": [
-                  {
-                     "type": "object"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Args passed to the HTTP client.",
-               "title": "Clientargs"
-            },
-            "asyncClientArgs": {
-               "anyOf": [
-                  {
-                     "type": "object"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Args passed to the async HTTP client.",
-               "title": "Asyncclientargs"
-            },
-            "extraBody": {
-               "anyOf": [
-                  {
-                     "type": "object"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Extra parameters to add to the request body.\n      The structure must match the backend API's request structure.\n      - VertexAI backend API docs: https://cloud.google.com/vertex-ai/docs/reference/rest\n      - GeminiAPI backend API docs: https://ai.google.dev/api/rest",
-               "title": "Extrabody"
-            },
-            "retryOptions": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/HttpRetryOptions"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "HTTP retry options for the request."
-            }
-         },
-         "title": "HttpOptions",
-         "type": "object"
-      },
-      "HttpRetryOptions": {
-         "additionalProperties": false,
-         "description": "HTTP retry options to be used in each of the requests.",
-         "properties": {
-            "attempts": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Maximum number of attempts, including the original request.\n      If 0 or 1, it means no retries.",
-               "title": "Attempts"
-            },
-            "initialDelay": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Initial delay before the first retry, in fractions of a second.",
-               "title": "Initialdelay"
-            },
-            "maxDelay": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Maximum delay between retries, in fractions of a second.",
-               "title": "Maxdelay"
-            },
-            "expBase": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Multiplier by which the delay increases after each attempt.",
-               "title": "Expbase"
-            },
-            "jitter": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Randomness factor for the delay.",
-               "title": "Jitter"
-            },
-            "httpStatusCodes": {
-               "anyOf": [
-                  {
-                     "items": {
-                        "type": "integer"
-                     },
-                     "type": "array"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "List of HTTP status codes that should trigger a retry.\n      If not specified, a default set of retryable codes may be used.",
-               "title": "Httpstatuscodes"
-            }
-         },
-         "title": "HttpRetryOptions",
-         "type": "object"
-      },
       "TuningDataset": {
          "additionalProperties": false,
          "description": "Supervised fine-tuning training dataset.",
@@ -42554,39 +43010,6 @@ 
    Submodules         },
          "title": "TuningExample",
          "type": "object"
-      },
-      "TuningValidationDataset": {
-         "additionalProperties": false,
-         "properties": {
-            "gcsUri": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "GCS URI of the file containing validation dataset in JSONL format.",
-               "title": "Gcsuri"
-            },
-            "vertexDatasetResource": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The resource name of the Vertex Multimodal Dataset that is used as training dataset. Example: 'projects/my-project-id-or-number/locations/my-location/datasets/my-dataset-id'.",
-               "title": "Vertexdatasetresource"
-            }
-         },
-         "title": "TuningValidationDataset",
-         "type": "object"
       }
    },
    "additionalProperties": false
@@ -42647,6 +43070,67 @@ 
    Submodules
+
    
+pydantic model genai.types.CustomOutputFormatConfig
    
+
    Bases: BaseModel
    
+
    Config for custom output format.
    
+
    Create a new model by parsing and validating input data from keyword arguments.
    
+
    Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be
+validated to form a valid model.
    
+
    self is explicitly positional-only to allow self as a field name.
    
+
    
+Show JSON schema
    {
+   "title": "CustomOutputFormatConfig",
+   "description": "Config for custom output format.",
+   "type": "object",
+   "properties": {
+      "returnRawOutput": {
+         "anyOf": [
+            {
+               "type": "boolean"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "Optional. Whether to return raw output.",
+         "title": "Returnrawoutput"
+      }
+   },
+   "additionalProperties": false
+}
+
    
+
    
+
    
+
    Fields:
    
+
    
+
    
+
    
+
    
+
    
+field return_raw_output: Optional[bool] = None (alias 'returnRawOutput')
    
+
    Optional. Whether to return raw output.
    
+
    
+
+

    + +
    +
    +class genai.types.CustomOutputFormatConfigDict
    +

    Bases: TypedDict

    +

    Config for custom output format.

    +
    +
    +return_raw_output: Optional[bool]
    +

    Optional. Whether to return raw output.

    +
    + +
    +
    pydantic model genai.types.DatasetDistribution
    @@ -46213,422 +46697,6 @@

    Submodules -
    -pydantic model genai.types.DistillationHyperParameters
    -

    Bases: BaseModel

    -

    Hyperparameters for Distillation.

    -

    Create a new model by parsing and validating input data from keyword arguments.

    -

    Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be -validated to form a valid model.

    -

    self is explicitly positional-only to allow self as a field name.

    -

    -Show JSON schema
    {
-   "title": "DistillationHyperParameters",
-   "description": "Hyperparameters for Distillation.",
-   "type": "object",
-   "properties": {
-      "adapterSize": {
-         "anyOf": [
-            {
-               "$ref": "#/$defs/AdapterSize"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "Optional. Adapter size for distillation."
-      },
-      "epochCount": {
-         "anyOf": [
-            {
-               "type": "integer"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "Optional. Number of complete passes the model makes over the entire training dataset during training.",
-         "title": "Epochcount"
-      },
-      "learningRateMultiplier": {
-         "anyOf": [
-            {
-               "type": "number"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "Optional. Multiplier for adjusting the default learning rate.",
-         "title": "Learningratemultiplier"
-      }
-   },
-   "$defs": {
-      "AdapterSize": {
-         "description": "Optional. Adapter size for tuning.",
-         "enum": [
-            "ADAPTER_SIZE_UNSPECIFIED",
-            "ADAPTER_SIZE_ONE",
-            "ADAPTER_SIZE_TWO",
-            "ADAPTER_SIZE_FOUR",
-            "ADAPTER_SIZE_EIGHT",
-            "ADAPTER_SIZE_SIXTEEN",
-            "ADAPTER_SIZE_THIRTY_TWO"
-         ],
-         "title": "AdapterSize",
-         "type": "string"
-      }
-   },
-   "additionalProperties": false
-}
-
    -
    -

    -
    Fields:
    -
    -
    -
    -
    -
    -field adapter_size: Optional[AdapterSize] = None (alias 'adapterSize')
    -

    Optional. Adapter size for distillation.

    -
    - -
    -
    -field epoch_count: Optional[int] = None (alias 'epochCount')
    -

    Optional. Number of complete passes the model makes over the entire training dataset during training.

    -
    - -
    -
    -field learning_rate_multiplier: Optional[float] = None (alias 'learningRateMultiplier')
    -

    Optional. Multiplier for adjusting the default learning rate.

    -
    - -

    - -
    -
    -class genai.types.DistillationHyperParametersDict
    -

    Bases: TypedDict

    -

    Hyperparameters for Distillation.

    -
    -
    -adapter_size: Optional[AdapterSize]
    -

    Optional. Adapter size for distillation.

    -
    - -
    -
    -epoch_count: Optional[int]
    -

    Optional. Number of complete passes the model makes over the entire training dataset during training.

    -
    - -
    -
    -learning_rate_multiplier: Optional[float]
    -

    Optional. Multiplier for adjusting the default learning rate.

    -
    - -
    - -
    -
    -pydantic model genai.types.DistillationSpec
    -

    Bases: BaseModel

    -

    Tuning Spec for Distillation.

    -

    Create a new model by parsing and validating input data from keyword arguments.

    -

    Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be -validated to form a valid model.

    -

    self is explicitly positional-only to allow self as a field name.

    -

    -Show JSON schema
    {
-   "title": "DistillationSpec",
-   "description": "Tuning Spec for Distillation.",
-   "type": "object",
-   "properties": {
-      "baseTeacherModel": {
-         "anyOf": [
-            {
-               "type": "string"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "The base teacher model that is being distilled. See [Supported models](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/tuning#supported_models).",
-         "title": "Baseteachermodel"
-      },
-      "hyperParameters": {
-         "anyOf": [
-            {
-               "$ref": "#/$defs/DistillationHyperParameters"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "Optional. Hyperparameters for Distillation."
-      },
-      "pipelineRootDirectory": {
-         "anyOf": [
-            {
-               "type": "string"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "Deprecated. A path in a Cloud Storage bucket, which will be treated as the root output directory of the distillation pipeline. It is used by the system to generate the paths of output artifacts.",
-         "title": "Pipelinerootdirectory"
-      },
-      "studentModel": {
-         "anyOf": [
-            {
-               "type": "string"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "The student model that is being tuned, e.g., \"google/gemma-2b-1.1-it\". Deprecated. Use base_model instead.",
-         "title": "Studentmodel"
-      },
-      "trainingDatasetUri": {
-         "anyOf": [
-            {
-               "type": "string"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "Deprecated. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file.",
-         "title": "Trainingdataseturi"
-      },
-      "tunedTeacherModelSource": {
-         "anyOf": [
-            {
-               "type": "string"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "The resource name of the Tuned teacher model. Format: `projects/{project}/locations/{location}/models/{model}`.",
-         "title": "Tunedteachermodelsource"
-      },
-      "validationDatasetUri": {
-         "anyOf": [
-            {
-               "type": "string"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file.",
-         "title": "Validationdataseturi"
-      }
-   },
-   "$defs": {
-      "AdapterSize": {
-         "description": "Optional. Adapter size for tuning.",
-         "enum": [
-            "ADAPTER_SIZE_UNSPECIFIED",
-            "ADAPTER_SIZE_ONE",
-            "ADAPTER_SIZE_TWO",
-            "ADAPTER_SIZE_FOUR",
-            "ADAPTER_SIZE_EIGHT",
-            "ADAPTER_SIZE_SIXTEEN",
-            "ADAPTER_SIZE_THIRTY_TWO"
-         ],
-         "title": "AdapterSize",
-         "type": "string"
-      },
-      "DistillationHyperParameters": {
-         "additionalProperties": false,
-         "description": "Hyperparameters for Distillation.",
-         "properties": {
-            "adapterSize": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/AdapterSize"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Adapter size for distillation."
-            },
-            "epochCount": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Number of complete passes the model makes over the entire training dataset during training.",
-               "title": "Epochcount"
-            },
-            "learningRateMultiplier": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Multiplier for adjusting the default learning rate.",
-               "title": "Learningratemultiplier"
-            }
-         },
-         "title": "DistillationHyperParameters",
-         "type": "object"
-      }
-   },
-   "additionalProperties": false
-}
-
    -
    -

    -
    Fields:
    -
    -
    -
    -
    -
    -field base_teacher_model: Optional[str] = None (alias 'baseTeacherModel')
    -

    The base teacher model that is being distilled. See [Supported models](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/tuning#supported_models).

    -
    - -
    -
    -field hyper_parameters: Optional[DistillationHyperParameters] = None (alias 'hyperParameters')
    -

    Optional. Hyperparameters for Distillation.

    -
    - -
    -
    -field pipeline_root_directory: Optional[str] = None (alias 'pipelineRootDirectory')
    -

    Deprecated. A path in a Cloud Storage bucket, which will be treated as the root output directory of the distillation pipeline. It is used by the system to generate the paths of output artifacts.

    -
    - -
    -
    -field student_model: Optional[str] = None (alias 'studentModel')
    -

    The student model that is being tuned, e.g., “google/gemma-2b-1.1-it”. Deprecated. Use base_model instead.

    -
    - -
    -
    -field training_dataset_uri: Optional[str] = None (alias 'trainingDatasetUri')
    -

    Deprecated. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file.

    -
    - -
    -
    -field tuned_teacher_model_source: Optional[str] = None (alias 'tunedTeacherModelSource')
    -

    The resource name of the Tuned teacher model. Format: projects/{project}/locations/{location}/models/{model}.

    -
    - -
    -
    -field validation_dataset_uri: Optional[str] = None (alias 'validationDatasetUri')
    -

    Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file.

    -
    - -
    - -
    -
    -class genai.types.DistillationSpecDict
    -

    Bases: TypedDict

    -

    Tuning Spec for Distillation.

    -
    -
    -base_teacher_model: Optional[str]
    -

    //cloud.google.com/vertex-ai/generative-ai/docs/model-reference/tuning#supported_models).

    -
    -
    Type:
    -

    The base teacher model that is being distilled. See [Supported models](https

    -
    -
    -
    - -
    -
    -hyper_parameters: Optional[DistillationHyperParametersDict]
    -

    Optional. Hyperparameters for Distillation.

    -
    - -
    -
    -pipeline_root_directory: Optional[str]
    -

    Deprecated. A path in a Cloud Storage bucket, which will be treated as the root output directory of the distillation pipeline. It is used by the system to generate the paths of output artifacts.

    -
    - -
    -
    -student_model: Optional[str]
    -

    The student model that is being tuned, e.g., “google/gemma-2b-1.1-it”. Deprecated. Use base_model instead.

    -
    - -
    -
    -training_dataset_uri: Optional[str]
    -

    Deprecated. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file.

    -
    - -
    -
    -tuned_teacher_model_source: Optional[str]
    -

    projects/{project}/locations/{location}/models/{model}.

    -
    -
    Type:
    -

    The resource name of the Tuned teacher model. Format

    -
    -
    -
    - -
    -
    -validation_dataset_uri: Optional[str]
    -

    Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file.

    -
    - -
    -
    pydantic model genai.types.DownloadFileConfig
    @@ -48996,18 +49064,351 @@

    Submodules "title": "EnterpriseWebSearch", "description": "Tool to search public web data, powered by Vertex AI Search and Sec4 compliance.", "type": "object", - "properties": {}, + "properties": { + "excludeDomains": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. List of domains to be excluded from the search results. The default limit is 2000 domains.", + "title": "Excludedomains" + } + }, "additionalProperties": false }

    -

    +

    +
    Fields:
    +
    +
    +
    +
    +
    +field exclude_domains: Optional[list[str]] = None (alias 'excludeDomains')
    +

    Optional. List of domains to be excluded from the search results. The default limit is 2000 domains.

    +
    + +
    class genai.types.EnterpriseWebSearchDict

    Bases: TypedDict

    Tool to search public web data, powered by Vertex AI Search and Sec4 compliance.

    +
    +
    +exclude_domains: Optional[list[str]]
    +

    Optional. List of domains to be excluded from the search results. The default limit is 2000 domains.

    +
    + +
    + +
    +
    +pydantic model genai.types.EntityLabel
    +

    Bases: BaseModel

    +

    An entity representing the segmented area.

    +

    Create a new model by parsing and validating input data from keyword arguments.

    +

    Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be +validated to form a valid model.

    +

    self is explicitly positional-only to allow self as a field name.

    +

    +Show JSON schema
    {
+   "title": "EntityLabel",
+   "description": "An entity representing the segmented area.",
+   "type": "object",
+   "properties": {
+      "label": {
+         "anyOf": [
+            {
+               "type": "string"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "The label of the segmented entity.",
+         "title": "Label"
+      },
+      "score": {
+         "anyOf": [
+            {
+               "type": "number"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "The confidence score of the detected label.",
+         "title": "Score"
+      }
+   },
+   "additionalProperties": false
+}
+
    +
    +

    +
    Fields:
    +
    +
    +
    +
    +
    +field label: Optional[str] = None
    +

    The label of the segmented entity.

    +
    + +
    +
    +field score: Optional[float] = None
    +

    The confidence score of the detected label.

    +
    + +
    + +
    +
    +class genai.types.EntityLabelDict
    +

    Bases: TypedDict

    +

    An entity representing the segmented area.

    +
    +
    +label: Optional[str]
    +

    The label of the segmented entity.

    +
    + +
    +
    +score: Optional[float]
    +

    The confidence score of the detected label.

    +
    + +
    + +
    +
    +class genai.types.Environment(*values)
    +

    Bases: CaseInSensitiveEnum

    +

    The environment being operated.

    +
    +
    +ENVIRONMENT_BROWSER = 'ENVIRONMENT_BROWSER'
    +

    Operates in a web browser.

    +
    + +
    +
    +ENVIRONMENT_UNSPECIFIED = 'ENVIRONMENT_UNSPECIFIED'
    +

    Defaults to browser.

    +
    + +
    + +
    +
    +pydantic model genai.types.EvaluationConfig
    +

    Bases: BaseModel

    +

    Evaluation config for tuning.

    +

    Create a new model by parsing and validating input data from keyword arguments.

    +

    Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be +validated to form a valid model.

    +

    self is explicitly positional-only to allow self as a field name.

    +

    +Show JSON schema
    {
+   "title": "EvaluationConfig",
+   "type": "object",
+   "properties": {
+      "metrics": {
+         "default": null,
+         "title": "Metrics"
+      },
+      "outputConfig": {
+         "anyOf": [
+            {
+               "$ref": "#/$defs/OutputConfig"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "Config for evaluation output."
+      },
+      "autoraterConfig": {
+         "anyOf": [
+            {
+               "$ref": "#/$defs/AutoraterConfig"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "Autorater config for evaluation."
+      }
+   },
+   "$defs": {
+      "AutoraterConfig": {
+         "additionalProperties": false,
+         "description": "Autorater config used for evaluation.",
+         "properties": {
+            "samplingCount": {
+               "anyOf": [
+                  {
+                     "type": "integer"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Number of samples for each instance in the dataset.\n  If not specified, the default is 4. Minimum value is 1, maximum value\n  is 32.",
+               "title": "Samplingcount"
+            },
+            "flipEnabled": {
+               "anyOf": [
+                  {
+                     "type": "boolean"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Optional. Default is true. Whether to flip the candidate and baseline\n  responses. This is only applicable to the pairwise metric. If enabled, also\n  provide PairwiseMetricSpec.candidate_response_field_name and\n  PairwiseMetricSpec.baseline_response_field_name. When rendering\n  PairwiseMetricSpec.metric_prompt_template, the candidate and baseline\n  fields will be flipped for half of the samples to reduce bias.",
+               "title": "Flipenabled"
+            },
+            "autoraterModel": {
+               "anyOf": [
+                  {
+                     "type": "string"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "The fully qualified name of the publisher model or tuned autorater\n  endpoint to use.\n\n  Publisher model format:\n  `projects/{project}/locations/{location}/publishers/*/models/*`\n\n  Tuned model endpoint format:\n  `projects/{project}/locations/{location}/endpoints/{endpoint}`",
+               "title": "Autoratermodel"
+            }
+         },
+         "title": "AutoraterConfig",
+         "type": "object"
+      },
+      "GcsDestination": {
+         "additionalProperties": false,
+         "description": "The Google Cloud Storage location where the output is to be written to.",
+         "properties": {
+            "outputUriPrefix": {
+               "anyOf": [
+                  {
+                     "type": "string"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Required. Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.",
+               "title": "Outputuriprefix"
+            }
+         },
+         "title": "GcsDestination",
+         "type": "object"
+      },
+      "OutputConfig": {
+         "additionalProperties": false,
+         "description": "Config for evaluation output.",
+         "properties": {
+            "gcsDestination": {
+               "anyOf": [
+                  {
+                     "$ref": "#/$defs/GcsDestination"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Cloud storage destination for evaluation output."
+            }
+         },
+         "title": "OutputConfig",
+         "type": "object"
+      }
+   },
+   "additionalProperties": false
+}
+
    +
    +

    +
    Fields:
    +
    +
    +
    +
    +
    +field autorater_config: Optional[AutoraterConfig] = None (alias 'autoraterConfig')
    +

    Autorater config for evaluation.

    +
    + +
    +
    +field metrics: Optional[list[Metric]] = None
    +

    The metrics used for evaluation.

    +
    + +
    +
    +field output_config: Optional[OutputConfig] = None (alias 'outputConfig')
    +

    Config for evaluation output.

    +
    + +
    + +
    +
    +class genai.types.EvaluationConfigDict
    +

    Bases: TypedDict

    +

    Evaluation config for tuning.

    +
    +
    +autorater_config: Optional[AutoraterConfigDict]
    +

    Autorater config for evaluation.

    +
    + +
    +
    +metrics: Optional[list[MetricDict]]
    +

    The metrics used for evaluation.

    +
    + +
    +
    +output_config: Optional[OutputConfigDict]
    +

    Config for evaluation output.

    +
    +
    @@ -51719,6 +52120,13 @@

    Submodules classmethod from_callable(*, client, callable, behavior=None)

    Converts a Callable to a FunctionDeclaration based on the client.

    +

    Note: For best results prefer +[Google-style +docstring](https://google.github.io/styleguide/pyguide.html#383-functions-and-methods) +when describing arguments. This function does not parse argument +descriptions into the property description slots of the resulting structure. +Instead it sends the whole docstring in the top-level function description. +Google-style docstring are closest to what the model is trained on.

    Return type:

    FunctionDeclaration

    @@ -52023,6 +52431,79 @@

    Submodules +
    +pydantic model genai.types.GcsDestination
    +

    Bases: BaseModel

    +

    The Google Cloud Storage location where the output is to be written to.

    +

    Create a new model by parsing and validating input data from keyword arguments.

    +

    Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be +validated to form a valid model.

    +

    self is explicitly positional-only to allow self as a field name.

    +

    +Show JSON schema
    {
+   "title": "GcsDestination",
+   "description": "The Google Cloud Storage location where the output is to be written to.",
+   "type": "object",
+   "properties": {
+      "outputUriPrefix": {
+         "anyOf": [
+            {
+               "type": "string"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "Required. Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.",
+         "title": "Outputuriprefix"
+      }
+   },
+   "additionalProperties": false
+}
+
    +
    +

    +
    Fields:
    +
    +
    +
    Validators:
    +
      +

    • _validate_gcs_path » all fields

      • +
    +
    +
    +
    +
    +field output_uri_prefix: Optional[str] = None (alias 'outputUriPrefix')
    +

    Required. Google Cloud Storage URI to output directory. If the uri doesn’t end with ‘/’, a ‘/’ will be automatically appended. The directory is created if it doesn’t exist.

    +
    +
    Validated by:
    +
      +

    • _validate_gcs_path

      • +
    +
    +
    +
    + +

    + +
    +
    +class genai.types.GcsDestinationDict
    +

    Bases: TypedDict

    +

    The Google Cloud Storage location where the output is to be written to.

    +
    +
    +output_uri_prefix: Optional[str]
    +

    Required. Google Cloud Storage URI to output directory. If the uri doesn’t end with ‘/’, a ‘/’ will be automatically appended. The directory is created if it doesn’t exist.

    +
    + +
    +
    pydantic model genai.types.GeminiPreferenceExample
    @@ -54241,10 +54722,36 @@

    Submodules "EnterpriseWebSearch": { "additionalProperties": false, "description": "Tool to search public web data, powered by Vertex AI Search and Sec4 compliance.", - "properties": {}, + "properties": { + "excludeDomains": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. List of domains to be excluded from the search results. The default limit is 2000 domains.", + "title": "Excludedomains" + } + }, "title": "EnterpriseWebSearch", "type": "object" }, + "Environment": { + "description": "The environment being operated.", + "enum": [ + "ENVIRONMENT_UNSPECIFIED", + "ENVIRONMENT_BROWSER" + ], + "title": "Environment", + "type": "string" + }, "ExecutableCode": { "additionalProperties": false, "description": "Code generated by the model that is meant to be executed, and the result returned to the model.\n\nGenerated when using the [CodeExecution] tool, in which the code will be\nautomatically executed, and a corresponding [CodeExecutionResult] will also be\ngenerated.", @@ -55116,6 +55623,22 @@

    Submodules ], "default": null, "description": "Optional. Filter search results to a specific time range.\n If customers set a start time, they must set an end time (and vice versa).\n " + }, + "excludeDomains": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. List of domains to be excluded from the search results.\n The default limit is 2000 domains.", + "title": "Excludedomains" } }, "title": "GoogleSearch", @@ -56545,6 +57068,26 @@

    Submodules "title": "ToolCodeExecution", "type": "object" }, + "ToolComputerUse": { + "additionalProperties": false, + "description": "Tool to support computer use.", + "properties": { + "environment": { + "anyOf": [ + { + "$ref": "#/$defs/Environment" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Required. The environment being operated." + } + }, + "title": "ToolComputerUse", + "type": "object" + }, "ToolConfig": { "additionalProperties": false, "description": "Tool config.\n\nThis config is shared for all tools provided in the request.", @@ -56996,6 +57539,18 @@

    Submodules "default": null, "description": "Optional. Tool to support URL context retrieval." }, + "computerUse": { + "anyOf": [ + { + "$ref": "#/$defs/ToolComputerUse" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. Tool to support the model interacting directly with the\n computer. If enabled, it automatically populates computer-use specific\n Function Declarations." + }, "codeExecution": { "anyOf": [ { @@ -58875,6 +59430,19 @@

    Submodules "additionalProperties": false, "description": "Chunk from context retrieved by the retrieval tools.", "properties": { + "documentName": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Output only. The full document name for the referenced Vertex AI Search document.", + "title": "Documentname" + }, "ragChunk": { "anyOf": [ { @@ -63200,6 +63768,179 @@

    Submodules +
    +pydantic model genai.types.GeneratedImageMask
    +

    Bases: BaseModel

    +

    A generated image mask.

    +

    Create a new model by parsing and validating input data from keyword arguments.

    +

    Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be +validated to form a valid model.

    +

    self is explicitly positional-only to allow self as a field name.

    +

    +Show JSON schema
    {
+   "title": "GeneratedImageMask",
+   "description": "A generated image mask.",
+   "type": "object",
+   "properties": {
+      "mask": {
+         "anyOf": [
+            {
+               "$ref": "#/$defs/Image"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "The generated image mask."
+      },
+      "labels": {
+         "anyOf": [
+            {
+               "items": {
+                  "$ref": "#/$defs/EntityLabel"
+               },
+               "type": "array"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "The detected entities on the segmented area.",
+         "title": "Labels"
+      }
+   },
+   "$defs": {
+      "EntityLabel": {
+         "additionalProperties": false,
+         "description": "An entity representing the segmented area.",
+         "properties": {
+            "label": {
+               "anyOf": [
+                  {
+                     "type": "string"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "The label of the segmented entity.",
+               "title": "Label"
+            },
+            "score": {
+               "anyOf": [
+                  {
+                     "type": "number"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "The confidence score of the detected label.",
+               "title": "Score"
+            }
+         },
+         "title": "EntityLabel",
+         "type": "object"
+      },
+      "Image": {
+         "additionalProperties": false,
+         "description": "An image.",
+         "properties": {
+            "gcsUri": {
+               "anyOf": [
+                  {
+                     "type": "string"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "The Cloud Storage URI of the image. ``Image`` can contain a value\n      for this field or the ``image_bytes`` field but not both.\n      ",
+               "title": "Gcsuri"
+            },
+            "imageBytes": {
+               "anyOf": [
+                  {
+                     "format": "base64url",
+                     "type": "string"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "The image bytes data. ``Image`` can contain a value for this field\n      or the ``gcs_uri`` field but not both.\n      ",
+               "title": "Imagebytes"
+            },
+            "mimeType": {
+               "anyOf": [
+                  {
+                     "type": "string"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "The MIME type of the image.",
+               "title": "Mimetype"
+            }
+         },
+         "title": "Image",
+         "type": "object"
+      }
+   },
+   "additionalProperties": false
+}
+
    +
    +

    +
    Fields:
    +
    +
    +
    +
    +
    +field labels: Optional[list[EntityLabel]] = None
    +

    The detected entities on the segmented area.

    +
    + +
    +
    +field mask: Optional[Image] = None
    +

    The generated image mask.

    +
    + +

    + +
    +
    +class genai.types.GeneratedImageMaskDict
    +

    Bases: TypedDict

    +

    A generated image mask.

    +
    +
    +labels: Optional[list[EntityLabelDict]]
    +

    The detected entities on the segmented area.

    +
    + +
    +
    +mask: Optional[ImageDict]
    +

    The generated image mask.

    +
    + +
    +
    pydantic model genai.types.GeneratedVideo
    @@ -66992,6 +67733,22 @@

    Submodules ], "default": null, "description": "Optional. Filter search results to a specific time range.\n If customers set a start time, they must set an end time (and vice versa).\n " + }, + "excludeDomains": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. List of domains to be excluded from the search results.\n The default limit is 2000 domains.", + "title": "Excludedomains" } }, "$defs": { @@ -67039,10 +67796,18 @@

    Submodules
    Fields:

    +
    +
    +field exclude_domains: Optional[list[str]] = None (alias 'excludeDomains')
    +

    Optional. List of domains to be excluded from the search results. +The default limit is 2000 domains.

    +
    +
    field time_range_filter: Optional[Interval] = None (alias 'timeRangeFilter')
    @@ -67057,6 +67822,13 @@

    Submodulesclass genai.types.GoogleSearchDict

    Bases: TypedDict

    Tool to support Google Search in Model. Powered by Google.

    +
    +
    +exclude_domains: Optional[list[str]]
    +

    Optional. List of domains to be excluded from the search results. +The default limit is 2000 domains.

    +
    +
    time_range_filter: Optional[IntervalDict]
    @@ -67582,6 +68354,19 @@

    Submodules "additionalProperties": false, "description": "Chunk from context retrieved by the retrieval tools.", "properties": { + "documentName": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Output only. The full document name for the referenced Vertex AI Search document.", + "title": "Documentname" + }, "ragChunk": { "anyOf": [ { @@ -68684,6 +69469,19 @@

    Submodules "description": "Chunk from context retrieved by the retrieval tools.", "type": "object", "properties": { + "documentName": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Output only. The full document name for the referenced Vertex AI Search document.", + "title": "Documentname" + }, "ragChunk": { "anyOf": [ { @@ -68812,6 +69610,7 @@

    Submodules
    Fields:

    +
    field rag_chunk: Optional[RagChunk] = None (alias 'ragChunk')
    @@ -68850,6 +69655,12 @@

    Submodulesclass genai.types.GroundingChunkRetrievedContextDict

    Bases: TypedDict

    Chunk from context retrieved by the retrieval tools.

    +
    +
    +document_name: Optional[str]
    +

    Output only. The full document name for the referenced Vertex AI Search document.

    +
    +
    rag_chunk: Optional[RagChunkDict]
    @@ -69383,6 +70194,19 @@

    Submodules "additionalProperties": false, "description": "Chunk from context retrieved by the retrieval tools.", "properties": { + "documentName": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Output only. The full document name for the referenced Vertex AI Search document.", + "title": "Documentname" + }, "ragChunk": { "anyOf": [ { @@ -71720,10 +72544,36 @@

    Submodules "EnterpriseWebSearch": { "additionalProperties": false, "description": "Tool to search public web data, powered by Vertex AI Search and Sec4 compliance.", - "properties": {}, + "properties": { + "excludeDomains": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. List of domains to be excluded from the search results. The default limit is 2000 domains.", + "title": "Excludedomains" + } + }, "title": "EnterpriseWebSearch", "type": "object" }, + "Environment": { + "description": "The environment being operated.", + "enum": [ + "ENVIRONMENT_UNSPECIFIED", + "ENVIRONMENT_BROWSER" + ], + "title": "Environment", + "type": "string" + }, "ExecutableCode": { "additionalProperties": false, "description": "Code generated by the model that is meant to be executed, and the result returned to the model.\n\nGenerated when using the [CodeExecution] tool, in which the code will be\nautomatically executed, and a corresponding [CodeExecutionResult] will also be\ngenerated.", @@ -73024,6 +73874,22 @@

    Submodules ], "default": null, "description": "Optional. Filter search results to a specific time range.\n If customers set a start time, they must set an end time (and vice versa).\n " + }, + "excludeDomains": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. List of domains to be excluded from the search results.\n The default limit is 2000 domains.", + "title": "Excludedomains" } }, "title": "GoogleSearch", @@ -74453,6 +75319,26 @@

    Submodules "title": "ToolCodeExecution", "type": "object" }, + "ToolComputerUse": { + "additionalProperties": false, + "description": "Tool to support computer use.", + "properties": { + "environment": { + "anyOf": [ + { + "$ref": "#/$defs/Environment" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Required. The environment being operated." + } + }, + "title": "ToolComputerUse", + "type": "object" + }, "ToolConfig": { "additionalProperties": false, "description": "Tool config.\n\nThis config is shared for all tools provided in the request.", @@ -74904,6 +75790,18 @@

    Submodules "default": null, "description": "Optional. Tool to support URL context retrieval." }, + "computerUse": { + "anyOf": [ + { + "$ref": "#/$defs/ToolComputerUse" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. Tool to support the model interacting directly with the\n computer. If enabled, it automatically populates computer-use specific\n Function Declarations." + }, "codeExecution": { "anyOf": [ { @@ -76356,6 +77254,19 @@

    Submodules "additionalProperties": false, "description": "Chunk from context retrieved by the retrieval tools.", "properties": { + "documentName": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Output only. The full document name for the referenced Vertex AI Search document.", + "title": "Documentname" + }, "ragChunk": { "anyOf": [ { @@ -79897,10 +80808,36 @@

    Submodules "EnterpriseWebSearch": { "additionalProperties": false, "description": "Tool to search public web data, powered by Vertex AI Search and Sec4 compliance.", - "properties": {}, + "properties": { + "excludeDomains": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. List of domains to be excluded from the search results. The default limit is 2000 domains.", + "title": "Excludedomains" + } + }, "title": "EnterpriseWebSearch", "type": "object" }, + "Environment": { + "description": "The environment being operated.", + "enum": [ + "ENVIRONMENT_UNSPECIFIED", + "ENVIRONMENT_BROWSER" + ], + "title": "Environment", + "type": "string" + }, "ExecutableCode": { "additionalProperties": false, "description": "Code generated by the model that is meant to be executed, and the result returned to the model.\n\nGenerated when using the [CodeExecution] tool, in which the code will be\nautomatically executed, and a corresponding [CodeExecutionResult] will also be\ngenerated.", @@ -81568,6 +82505,22 @@

    Submodules ], "default": null, "description": "Optional. Filter search results to a specific time range.\n If customers set a start time, they must set an end time (and vice versa).\n " + }, + "excludeDomains": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. List of domains to be excluded from the search results.\n The default limit is 2000 domains.", + "title": "Excludedomains" } }, "title": "GoogleSearch", @@ -81916,6 +82869,19 @@

    Submodules "additionalProperties": false, "description": "Chunk from context retrieved by the retrieval tools.", "properties": { + "documentName": { + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Output only. The full document name for the referenced Vertex AI Search document.", + "title": "Documentname" + }, "ragChunk": { "anyOf": [ { @@ -84286,6 +85252,26 @@

    Submodules "title": "ToolCodeExecution", "type": "object" }, + "ToolComputerUse": { + "additionalProperties": false, + "description": "Tool to support computer use.", + "properties": { + "environment": { + "anyOf": [ + { + "$ref": "#/$defs/Environment" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Required. The environment being operated." + } + }, + "title": "ToolComputerUse", + "type": "object" + }, "ToolConfig": { "additionalProperties": false, "description": "Tool config.\n\nThis config is shared for all tools provided in the request.", @@ -84816,6 +85802,18 @@

    Submodules "default": null, "description": "Optional. Tool to support URL context retrieval." }, + "computerUse": { + "anyOf": [ + { + "$ref": "#/$defs/ToolComputerUse" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. Tool to support the model interacting directly with the\n computer. If enabled, it automatically populates computer-use specific\n Function Declarations." + }, "codeExecution": { "anyOf": [ { @@ -87455,7 +88453,6 @@

    Submodules Show JSON schema
    {
    "title": "ListTuningJobsResponse",
-   "description": "Response for the list tuning jobs method.",
    "type": "object",
    "properties": {
       "sdkHttpResponse": {
@@ -87484,10 +88481,130 @@ 
    Submodules         "title": "Nextpagetoken"
       },
       "tuningJobs": {
+         "default": null,
+         "title": "Tuningjobs"
+      }
+   },
+   "$defs": {
+      "HttpResponse": {
+         "additionalProperties": false,
+         "description": "A wrapper class for the http response.",
+         "properties": {
+            "headers": {
+               "anyOf": [
+                  {
+                     "additionalProperties": {
+                        "type": "string"
+                     },
+                     "type": "object"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Used to retain the processed HTTP headers in the response.",
+               "title": "Headers"
+            },
+            "body": {
+               "anyOf": [
+                  {
+                     "type": "string"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "The raw HTTP response body, in JSON format.",
+               "title": "Body"
+            }
+         },
+         "title": "HttpResponse",
+         "type": "object"
+      }
+   },
+   "additionalProperties": false
+}
+
    +
    +

    +
    Fields:
    +
    +
    +
    +
    +
    +field next_page_token: Optional[str] = None (alias 'nextPageToken')
    +

    A token to retrieve the next page of results. Pass to ListTuningJobsRequest.page_token to obtain that page.

    +
    + +
    +
    +field sdk_http_response: Optional[HttpResponse] = None (alias 'sdkHttpResponse')
    +

    Used to retain the full HTTP response.

    +
    + +
    +
    +field tuning_jobs: Optional[list[TuningJob]] = None (alias 'tuningJobs')
    +

    List of TuningJobs in the requested page.

    +
    + +

    + +
    +
    +class genai.types.ListTuningJobsResponseDict
    +

    Bases: TypedDict

    +

    Response for the list tuning jobs method.

    +
    +
    +next_page_token: Optional[str]
    +

    A token to retrieve the next page of results. Pass to ListTuningJobsRequest.page_token to obtain that page.

    +
    + +
    +
    +sdk_http_response: Optional[HttpResponseDict]
    +

    Used to retain the full HTTP response.

    +
    + +
    +
    +tuning_jobs: Optional[list[TuningJobDict]]
    +

    List of TuningJobs in the requested page.

    +
    + +
    + +
    +
    +pydantic model genai.types.LiveClientContent
    +

    Bases: BaseModel

    +

    Incremental update of the current conversation delivered from the client.

    +

    All the content here will unconditionally be appended to the conversation +history and used as part of the prompt to the model to generate content.

    +

    A message here will interrupt any current model generation.

    +

    Create a new model by parsing and validating input data from keyword arguments.

    +

    Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be +validated to form a valid model.

    +

    self is explicitly positional-only to allow self as a field name.

    +

    +Show JSON schema
    {
+   "title": "LiveClientContent",
+   "description": "Incremental update of the current conversation delivered from the client.\n\nAll the content here will unconditionally be appended to the conversation\nhistory and used as part of the prompt to the model to generate content.\n\nA message here will interrupt any current model generation.",
+   "type": "object",
+   "properties": {
+      "turns": {
          "anyOf": [
             {
                "items": {
-                  "$ref": "#/$defs/TuningJob"
+                  "$ref": "#/$defs/Content"
                },
                "type": "array"
             },
@@ -87496,2725 +88613,21 @@ 
    Submodules            }
          ],
          "default": null,
-         "description": "List of TuningJobs in the requested page.",
-         "title": "Tuningjobs"
-      }
-   },
-   "$defs": {
-      "AdapterSize": {
-         "description": "Optional. Adapter size for tuning.",
-         "enum": [
-            "ADAPTER_SIZE_UNSPECIFIED",
-            "ADAPTER_SIZE_ONE",
-            "ADAPTER_SIZE_TWO",
-            "ADAPTER_SIZE_FOUR",
-            "ADAPTER_SIZE_EIGHT",
-            "ADAPTER_SIZE_SIXTEEN",
-            "ADAPTER_SIZE_THIRTY_TWO"
-         ],
-         "title": "AdapterSize",
-         "type": "string"
-      },
-      "Blob": {
-         "additionalProperties": false,
-         "description": "Content blob.",
-         "properties": {
-            "displayName": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is not currently used in the Gemini GenerateContent calls.",
-               "title": "Displayname"
-            },
-            "data": {
-               "anyOf": [
-                  {
-                     "format": "base64url",
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Required. Raw bytes.",
-               "title": "Data"
-            },
-            "mimeType": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Required. The IANA standard MIME type of the source data.",
-               "title": "Mimetype"
-            }
-         },
-         "title": "Blob",
-         "type": "object"
-      },
-      "CodeExecutionResult": {
-         "additionalProperties": false,
-         "description": "Result of executing the [ExecutableCode].\n\nOnly generated when using the [CodeExecution] tool, and always follows a\n`part` containing the [ExecutableCode].",
-         "properties": {
-            "outcome": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/Outcome"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Required. Outcome of the code execution."
-            },
-            "output": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Contains stdout when code execution is successful, stderr or other description otherwise.",
-               "title": "Output"
-            }
-         },
-         "title": "CodeExecutionResult",
-         "type": "object"
-      },
-      "Content": {
-         "additionalProperties": false,
-         "description": "Contains the multi-part content of a message.",
-         "properties": {
-            "parts": {
-               "anyOf": [
-                  {
-                     "items": {
-                        "$ref": "#/$defs/Part"
-                     },
-                     "type": "array"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "List of parts that constitute a single message. Each part may have\n      a different IANA MIME type.",
-               "title": "Parts"
-            },
-            "role": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. The producer of the content. Must be either 'user' or\n      'model'. Useful to set for multi-turn conversations, otherwise can be\n      empty. If role is not specified, SDK will determine the role.",
-               "title": "Role"
-            }
-         },
-         "title": "Content",
-         "type": "object"
-      },
-      "DatasetDistribution": {
-         "additionalProperties": false,
-         "description": "Distribution computed over a tuning dataset.",
-         "properties": {
-            "buckets": {
-               "anyOf": [
-                  {
-                     "items": {
-                        "$ref": "#/$defs/DatasetDistributionDistributionBucket"
-                     },
-                     "type": "array"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Defines the histogram bucket.",
-               "title": "Buckets"
-            },
-            "max": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. The maximum of the population values.",
-               "title": "Max"
-            },
-            "mean": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. The arithmetic mean of the values in the population.",
-               "title": "Mean"
-            },
-            "median": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. The median of the values in the population.",
-               "title": "Median"
-            },
-            "min": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. The minimum of the population values.",
-               "title": "Min"
-            },
-            "p5": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. The 5th percentile of the values in the population.",
-               "title": "P5"
-            },
-            "p95": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. The 95th percentile of the values in the population.",
-               "title": "P95"
-            },
-            "sum": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Sum of a given population of values.",
-               "title": "Sum"
-            }
-         },
-         "title": "DatasetDistribution",
-         "type": "object"
-      },
-      "DatasetDistributionDistributionBucket": {
-         "additionalProperties": false,
-         "description": "Dataset bucket used to create a histogram for the distribution given a population of values.",
-         "properties": {
-            "count": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Number of values in the bucket.",
-               "title": "Count"
-            },
-            "left": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Left bound of the bucket.",
-               "title": "Left"
-            },
-            "right": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Right bound of the bucket.",
-               "title": "Right"
-            }
-         },
-         "title": "DatasetDistributionDistributionBucket",
-         "type": "object"
-      },
-      "DatasetStats": {
-         "additionalProperties": false,
-         "description": "Statistics computed over a tuning dataset.",
-         "properties": {
-            "totalBillableCharacterCount": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Number of billable characters in the tuning dataset.",
-               "title": "Totalbillablecharactercount"
-            },
-            "totalTuningCharacterCount": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Number of tuning characters in the tuning dataset.",
-               "title": "Totaltuningcharactercount"
-            },
-            "tuningDatasetExampleCount": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Number of examples in the tuning dataset.",
-               "title": "Tuningdatasetexamplecount"
-            },
-            "tuningStepCount": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Number of tuning steps for this Tuning Job.",
-               "title": "Tuningstepcount"
-            },
-            "userDatasetExamples": {
-               "anyOf": [
-                  {
-                     "items": {
-                        "$ref": "#/$defs/Content"
-                     },
-                     "type": "array"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Sample user messages in the training dataset uri.",
-               "title": "Userdatasetexamples"
-            },
-            "userInputTokenDistribution": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/DatasetDistribution"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Dataset distributions for the user input tokens."
-            },
-            "userMessagePerExampleDistribution": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/DatasetDistribution"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Dataset distributions for the messages per example."
-            },
-            "userOutputTokenDistribution": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/DatasetDistribution"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Dataset distributions for the user output tokens."
-            }
-         },
-         "title": "DatasetStats",
-         "type": "object"
-      },
-      "DistillationDataStats": {
-         "additionalProperties": false,
-         "description": "Statistics computed for datasets used for distillation.",
-         "properties": {
-            "trainingDatasetStats": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/DatasetStats"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Statistics computed for the training dataset."
-            }
-         },
-         "title": "DistillationDataStats",
-         "type": "object"
-      },
-      "DistillationHyperParameters": {
-         "additionalProperties": false,
-         "description": "Hyperparameters for Distillation.",
-         "properties": {
-            "adapterSize": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/AdapterSize"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Adapter size for distillation."
-            },
-            "epochCount": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Number of complete passes the model makes over the entire training dataset during training.",
-               "title": "Epochcount"
-            },
-            "learningRateMultiplier": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Multiplier for adjusting the default learning rate.",
-               "title": "Learningratemultiplier"
-            }
-         },
-         "title": "DistillationHyperParameters",
-         "type": "object"
-      },
-      "DistillationSpec": {
-         "additionalProperties": false,
-         "description": "Tuning Spec for Distillation.",
-         "properties": {
-            "baseTeacherModel": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The base teacher model that is being distilled. See [Supported models](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/tuning#supported_models).",
-               "title": "Baseteachermodel"
-            },
-            "hyperParameters": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/DistillationHyperParameters"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Hyperparameters for Distillation."
-            },
-            "pipelineRootDirectory": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Deprecated. A path in a Cloud Storage bucket, which will be treated as the root output directory of the distillation pipeline. It is used by the system to generate the paths of output artifacts.",
-               "title": "Pipelinerootdirectory"
-            },
-            "studentModel": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The student model that is being tuned, e.g., \"google/gemma-2b-1.1-it\". Deprecated. Use base_model instead.",
-               "title": "Studentmodel"
-            },
-            "trainingDatasetUri": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Deprecated. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file.",
-               "title": "Trainingdataseturi"
-            },
-            "tunedTeacherModelSource": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The resource name of the Tuned teacher model. Format: `projects/{project}/locations/{location}/models/{model}`.",
-               "title": "Tunedteachermodelsource"
-            },
-            "validationDatasetUri": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file.",
-               "title": "Validationdataseturi"
-            }
-         },
-         "title": "DistillationSpec",
-         "type": "object"
-      },
-      "EncryptionSpec": {
-         "additionalProperties": false,
-         "description": "Represents a customer-managed encryption key spec that can be applied to a top-level resource.",
-         "properties": {
-            "kmsKeyName": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.",
-               "title": "Kmskeyname"
-            }
-         },
-         "title": "EncryptionSpec",
-         "type": "object"
-      },
-      "ExecutableCode": {
-         "additionalProperties": false,
-         "description": "Code generated by the model that is meant to be executed, and the result returned to the model.\n\nGenerated when using the [CodeExecution] tool, in which the code will be\nautomatically executed, and a corresponding [CodeExecutionResult] will also be\ngenerated.",
-         "properties": {
-            "code": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Required. The code to be executed.",
-               "title": "Code"
-            },
-            "language": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/Language"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Required. Programming language of the `code`."
-            }
-         },
-         "title": "ExecutableCode",
-         "type": "object"
-      },
-      "FileData": {
-         "additionalProperties": false,
-         "description": "URI based data.",
-         "properties": {
-            "displayName": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. It is not currently used in the Gemini GenerateContent calls.",
-               "title": "Displayname"
-            },
-            "fileUri": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Required. URI.",
-               "title": "Fileuri"
-            },
-            "mimeType": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Required. The IANA standard MIME type of the source data.",
-               "title": "Mimetype"
-            }
-         },
-         "title": "FileData",
-         "type": "object"
-      },
-      "FunctionCall": {
-         "additionalProperties": false,
-         "description": "A function call.",
-         "properties": {
-            "id": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The unique id of the function call. If populated, the client to execute the\n   `function_call` and return the response with the matching `id`.",
-               "title": "Id"
-            },
-            "args": {
-               "anyOf": [
-                  {
-                     "type": "object"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.",
-               "title": "Args"
-            },
-            "name": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Required. The name of the function to call. Matches [FunctionDeclaration.name].",
-               "title": "Name"
-            }
-         },
-         "title": "FunctionCall",
-         "type": "object"
-      },
-      "FunctionResponse": {
-         "additionalProperties": false,
-         "description": "A function response.",
-         "properties": {
-            "willContinue": {
-               "anyOf": [
-                  {
-                     "type": "boolean"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Signals that function call continues, and more responses will be returned, turning the function call into a generator. Is only applicable to NON_BLOCKING function calls (see FunctionDeclaration.behavior for details), ignored otherwise. If false, the default, future responses will not be considered. Is only applicable to NON_BLOCKING function calls, is ignored otherwise. If set to false, future responses will not be considered. It is allowed to return empty `response` with `will_continue=False` to signal that the function call is finished.",
-               "title": "Willcontinue"
-            },
-            "scheduling": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/FunctionResponseScheduling"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Specifies how the response should be scheduled in the conversation. Only applicable to NON_BLOCKING function calls, is ignored otherwise. Defaults to WHEN_IDLE."
-            },
-            "id": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.",
-               "title": "Id"
-            },
-            "name": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].",
-               "title": "Name"
-            },
-            "response": {
-               "anyOf": [
-                  {
-                     "type": "object"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Required. The function response in JSON object format. Use \"output\" key to specify function output and \"error\" key to specify error details (if any). If \"output\" and \"error\" keys are not specified, then whole \"response\" is treated as function output.",
-               "title": "Response"
-            }
-         },
-         "title": "FunctionResponse",
-         "type": "object"
-      },
-      "FunctionResponseScheduling": {
-         "description": "Specifies how the response should be scheduled in the conversation.",
-         "enum": [
-            "SCHEDULING_UNSPECIFIED",
-            "SILENT",
-            "WHEN_IDLE",
-            "INTERRUPT"
-         ],
-         "title": "FunctionResponseScheduling",
-         "type": "string"
-      },
-      "GeminiPreferenceExample": {
-         "additionalProperties": false,
-         "description": "Input example for preference optimization.",
-         "properties": {
-            "completions": {
-               "anyOf": [
-                  {
-                     "items": {
-                        "$ref": "#/$defs/GeminiPreferenceExampleCompletion"
-                     },
-                     "type": "array"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "List of completions for a given prompt.",
-               "title": "Completions"
-            },
-            "contents": {
-               "anyOf": [
-                  {
-                     "items": {
-                        "$ref": "#/$defs/Content"
-                     },
-                     "type": "array"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Multi-turn contents that represents the Prompt.",
-               "title": "Contents"
-            }
-         },
-         "title": "GeminiPreferenceExample",
-         "type": "object"
-      },
-      "GeminiPreferenceExampleCompletion": {
-         "additionalProperties": false,
-         "description": "Completion and its preference score.",
-         "properties": {
-            "completion": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/Content"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Single turn completion for the given prompt."
-            },
-            "score": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The score for the given completion.",
-               "title": "Score"
-            }
-         },
-         "title": "GeminiPreferenceExampleCompletion",
-         "type": "object"
-      },
-      "GoogleRpcStatus": {
-         "additionalProperties": false,
-         "description": "The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs.\n\nIt is used by [gRPC](https://github.com/grpc). Each `Status` message contains\nthree pieces of data: error code, error message, and error details. You can\nfind out more about this error model and how to work with it in the [API\nDesign Guide](https://cloud.google.com/apis/design/errors).",
-         "properties": {
-            "code": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The status code, which should be an enum value of google.rpc.Code.",
-               "title": "Code"
-            },
-            "details": {
-               "anyOf": [
-                  {
-                     "items": {
-                        "type": "object"
-                     },
-                     "type": "array"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "A list of messages that carry the error details. There is a common set of message types for APIs to use.",
-               "title": "Details"
-            },
-            "message": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.",
-               "title": "Message"
-            }
-         },
-         "title": "GoogleRpcStatus",
-         "type": "object"
-      },
-      "HttpResponse": {
-         "additionalProperties": false,
-         "description": "A wrapper class for the http response.",
-         "properties": {
-            "headers": {
-               "anyOf": [
-                  {
-                     "additionalProperties": {
-                        "type": "string"
-                     },
-                     "type": "object"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Used to retain the processed HTTP headers in the response.",
-               "title": "Headers"
-            },
-            "body": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The raw HTTP response body, in JSON format.",
-               "title": "Body"
-            }
-         },
-         "title": "HttpResponse",
-         "type": "object"
-      },
-      "JobState": {
-         "description": "Job state.",
-         "enum": [
-            "JOB_STATE_UNSPECIFIED",
-            "JOB_STATE_QUEUED",
-            "JOB_STATE_PENDING",
-            "JOB_STATE_RUNNING",
-            "JOB_STATE_SUCCEEDED",
-            "JOB_STATE_FAILED",
-            "JOB_STATE_CANCELLING",
-            "JOB_STATE_CANCELLED",
-            "JOB_STATE_PAUSED",
-            "JOB_STATE_EXPIRED",
-            "JOB_STATE_UPDATING",
-            "JOB_STATE_PARTIALLY_SUCCEEDED"
-         ],
-         "title": "JobState",
-         "type": "string"
-      },
-      "Language": {
-         "description": "Required. Programming language of the `code`.",
-         "enum": [
-            "LANGUAGE_UNSPECIFIED",
-            "PYTHON"
-         ],
-         "title": "Language",
-         "type": "string"
-      },
-      "Outcome": {
-         "description": "Required. Outcome of the code execution.",
-         "enum": [
-            "OUTCOME_UNSPECIFIED",
-            "OUTCOME_OK",
-            "OUTCOME_FAILED",
-            "OUTCOME_DEADLINE_EXCEEDED"
-         ],
-         "title": "Outcome",
-         "type": "string"
-      },
-      "Part": {
-         "additionalProperties": false,
-         "description": "A datatype containing media content.\n\nExactly one field within a Part should be set, representing the specific type\nof content being conveyed. Using multiple fields within the same `Part`\ninstance is considered invalid.",
-         "properties": {
-            "videoMetadata": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/VideoMetadata"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Metadata for a given video."
-            },
-            "thought": {
-               "anyOf": [
-                  {
-                     "type": "boolean"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Indicates if the part is thought from the model.",
-               "title": "Thought"
-            },
-            "inlineData": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/Blob"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Inlined bytes data."
-            },
-            "fileData": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/FileData"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. URI based data."
-            },
-            "thoughtSignature": {
-               "anyOf": [
-                  {
-                     "format": "base64url",
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "An opaque signature for the thought so it can be reused in subsequent requests.",
-               "title": "Thoughtsignature"
-            },
-            "codeExecutionResult": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/CodeExecutionResult"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Result of executing the [ExecutableCode]."
-            },
-            "executableCode": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/ExecutableCode"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Code generated by the model that is meant to be executed."
-            },
-            "functionCall": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/FunctionCall"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values."
-            },
-            "functionResponse": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/FunctionResponse"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. The result output of a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function call. It is used as context to the model."
-            },
-            "text": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Text part (can be code).",
-               "title": "Text"
-            }
-         },
-         "title": "Part",
-         "type": "object"
-      },
-      "PartnerModelTuningSpec": {
-         "additionalProperties": false,
-         "description": "Tuning spec for Partner models.",
-         "properties": {
-            "hyperParameters": {
-               "anyOf": [
-                  {
-                     "type": "object"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Hyperparameters for tuning. The accepted hyper_parameters and their valid range of values will differ depending on the base model.",
-               "title": "Hyperparameters"
-            },
-            "trainingDatasetUri": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Required. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file.",
-               "title": "Trainingdataseturi"
-            },
-            "validationDatasetUri": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file.",
-               "title": "Validationdataseturi"
-            }
-         },
-         "title": "PartnerModelTuningSpec",
-         "type": "object"
-      },
-      "PreTunedModel": {
-         "additionalProperties": false,
-         "description": "A pre-tuned model for continuous tuning.",
-         "properties": {
-            "baseModel": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. The name of the base model this PreTunedModel was tuned from.",
-               "title": "Basemodel"
-            },
-            "checkpointId": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. The source checkpoint id. If not specified, the default checkpoint will be used.",
-               "title": "Checkpointid"
-            },
-            "tunedModelName": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The resource name of the Model. E.g., a model resource name with a specified version id or alias: `projects/{project}/locations/{location}/models/{model}@{version_id}` `projects/{project}/locations/{location}/models/{model}@{alias}` Or, omit the version id to use the default version: `projects/{project}/locations/{location}/models/{model}`",
-               "title": "Tunedmodelname"
-            }
-         },
-         "title": "PreTunedModel",
-         "type": "object"
-      },
-      "PreferenceOptimizationDataStats": {
-         "additionalProperties": false,
-         "description": "Statistics computed for datasets used for preference optimization.",
-         "properties": {
-            "scoreVariancePerExampleDistribution": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/DatasetDistribution"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Dataset distributions for scores variance per example."
-            },
-            "scoresDistribution": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/DatasetDistribution"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Dataset distributions for scores."
-            },
-            "totalBillableTokenCount": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Number of billable tokens in the tuning dataset.",
-               "title": "Totalbillabletokencount"
-            },
-            "tuningDatasetExampleCount": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Number of examples in the tuning dataset.",
-               "title": "Tuningdatasetexamplecount"
-            },
-            "tuningStepCount": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Number of tuning steps for this Tuning Job.",
-               "title": "Tuningstepcount"
-            },
-            "userDatasetExamples": {
-               "anyOf": [
-                  {
-                     "items": {
-                        "$ref": "#/$defs/GeminiPreferenceExample"
-                     },
-                     "type": "array"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Sample user examples in the training dataset.",
-               "title": "Userdatasetexamples"
-            },
-            "userInputTokenDistribution": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/DatasetDistribution"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Dataset distributions for the user input tokens."
-            },
-            "userOutputTokenDistribution": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/DatasetDistribution"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Dataset distributions for the user output tokens."
-            }
-         },
-         "title": "PreferenceOptimizationDataStats",
-         "type": "object"
-      },
-      "PreferenceOptimizationHyperParameters": {
-         "additionalProperties": false,
-         "description": "Hyperparameters for Preference Optimization.",
-         "properties": {
-            "adapterSize": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/AdapterSize"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Adapter size for preference optimization."
-            },
-            "beta": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Weight for KL Divergence regularization.",
-               "title": "Beta"
-            },
-            "epochCount": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Number of complete passes the model makes over the entire training dataset during training.",
-               "title": "Epochcount"
-            },
-            "learningRateMultiplier": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Multiplier for adjusting the default learning rate.",
-               "title": "Learningratemultiplier"
-            }
-         },
-         "title": "PreferenceOptimizationHyperParameters",
-         "type": "object"
-      },
-      "PreferenceOptimizationSpec": {
-         "additionalProperties": false,
-         "description": "Tuning Spec for Preference Optimization.",
-         "properties": {
-            "hyperParameters": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/PreferenceOptimizationHyperParameters"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Hyperparameters for Preference Optimization."
-            },
-            "trainingDatasetUri": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Required. Cloud Storage path to file containing training dataset for preference optimization tuning. The dataset must be formatted as a JSONL file.",
-               "title": "Trainingdataseturi"
-            },
-            "validationDatasetUri": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Cloud Storage path to file containing validation dataset for preference optimization tuning. The dataset must be formatted as a JSONL file.",
-               "title": "Validationdataseturi"
-            }
-         },
-         "title": "PreferenceOptimizationSpec",
-         "type": "object"
-      },
-      "SupervisedHyperParameters": {
-         "additionalProperties": false,
-         "description": "Hyperparameters for SFT.",
-         "properties": {
-            "adapterSize": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/AdapterSize"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Adapter size for tuning."
-            },
-            "batchSize": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Batch size for tuning. This feature is only available for open source models.",
-               "title": "Batchsize"
-            },
-            "epochCount": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Number of complete passes the model makes over the entire training dataset during training.",
-               "title": "Epochcount"
-            },
-            "learningRate": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Learning rate for tuning. Mutually exclusive with `learning_rate_multiplier`. This feature is only available for open source models.",
-               "title": "Learningrate"
-            },
-            "learningRateMultiplier": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Multiplier for adjusting the default learning rate. Mutually exclusive with `learning_rate`. This feature is only available for 1P models.",
-               "title": "Learningratemultiplier"
-            }
-         },
-         "title": "SupervisedHyperParameters",
-         "type": "object"
-      },
-      "SupervisedTuningDataStats": {
-         "additionalProperties": false,
-         "description": "Tuning data statistics for Supervised Tuning.",
-         "properties": {
-            "droppedExampleReasons": {
-               "anyOf": [
-                  {
-                     "items": {
-                        "type": "string"
-                     },
-                     "type": "array"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. For each index in `truncated_example_indices`, the user-facing reason why the example was dropped.",
-               "title": "Droppedexamplereasons"
-            },
-            "totalBillableCharacterCount": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Number of billable characters in the tuning dataset.",
-               "title": "Totalbillablecharactercount"
-            },
-            "totalBillableTokenCount": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Number of billable tokens in the tuning dataset.",
-               "title": "Totalbillabletokencount"
-            },
-            "totalTruncatedExampleCount": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. The number of examples in the dataset that have been dropped. An example can be dropped for reasons including: too many tokens, contains an invalid image, contains too many images, etc.",
-               "title": "Totaltruncatedexamplecount"
-            },
-            "totalTuningCharacterCount": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Number of tuning characters in the tuning dataset.",
-               "title": "Totaltuningcharactercount"
-            },
-            "truncatedExampleIndices": {
-               "anyOf": [
-                  {
-                     "items": {
-                        "type": "integer"
-                     },
-                     "type": "array"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. A partial sample of the indices (starting from 1) of the dropped examples.",
-               "title": "Truncatedexampleindices"
-            },
-            "tuningDatasetExampleCount": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Number of examples in the tuning dataset.",
-               "title": "Tuningdatasetexamplecount"
-            },
-            "tuningStepCount": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Number of tuning steps for this Tuning Job.",
-               "title": "Tuningstepcount"
-            },
-            "userDatasetExamples": {
-               "anyOf": [
-                  {
-                     "items": {
-                        "$ref": "#/$defs/Content"
-                     },
-                     "type": "array"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Sample user messages in the training dataset uri.",
-               "title": "Userdatasetexamples"
-            },
-            "userInputTokenDistribution": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/SupervisedTuningDatasetDistribution"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Dataset distributions for the user input tokens."
-            },
-            "userMessagePerExampleDistribution": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/SupervisedTuningDatasetDistribution"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Dataset distributions for the messages per example."
-            },
-            "userOutputTokenDistribution": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/SupervisedTuningDatasetDistribution"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Dataset distributions for the user output tokens."
-            }
-         },
-         "title": "SupervisedTuningDataStats",
-         "type": "object"
-      },
-      "SupervisedTuningDatasetDistribution": {
-         "additionalProperties": false,
-         "description": "Dataset distribution for Supervised Tuning.",
-         "properties": {
-            "billableSum": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Sum of a given population of values that are billable.",
-               "title": "Billablesum"
-            },
-            "buckets": {
-               "anyOf": [
-                  {
-                     "items": {
-                        "$ref": "#/$defs/SupervisedTuningDatasetDistributionDatasetBucket"
-                     },
-                     "type": "array"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Defines the histogram bucket.",
-               "title": "Buckets"
-            },
-            "max": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. The maximum of the population values.",
-               "title": "Max"
-            },
-            "mean": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. The arithmetic mean of the values in the population.",
-               "title": "Mean"
-            },
-            "median": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. The median of the values in the population.",
-               "title": "Median"
-            },
-            "min": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. The minimum of the population values.",
-               "title": "Min"
-            },
-            "p5": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. The 5th percentile of the values in the population.",
-               "title": "P5"
-            },
-            "p95": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. The 95th percentile of the values in the population.",
-               "title": "P95"
-            },
-            "sum": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Sum of a given population of values.",
-               "title": "Sum"
-            }
-         },
-         "title": "SupervisedTuningDatasetDistribution",
-         "type": "object"
-      },
-      "SupervisedTuningDatasetDistributionDatasetBucket": {
-         "additionalProperties": false,
-         "description": "Dataset bucket used to create a histogram for the distribution given a population of values.",
-         "properties": {
-            "count": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Number of values in the bucket.",
-               "title": "Count"
-            },
-            "left": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Left bound of the bucket.",
-               "title": "Left"
-            },
-            "right": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Right bound of the bucket.",
-               "title": "Right"
-            }
-         },
-         "title": "SupervisedTuningDatasetDistributionDatasetBucket",
-         "type": "object"
-      },
-      "SupervisedTuningSpec": {
-         "additionalProperties": false,
-         "description": "Tuning Spec for Supervised Tuning for first party models.",
-         "properties": {
-            "exportLastCheckpointOnly": {
-               "anyOf": [
-                  {
-                     "type": "boolean"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. If set to true, disable intermediate checkpoints for SFT and only the last checkpoint will be exported. Otherwise, enable intermediate checkpoints for SFT. Default is false.",
-               "title": "Exportlastcheckpointonly"
-            },
-            "hyperParameters": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/SupervisedHyperParameters"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Hyperparameters for SFT."
-            },
-            "trainingDatasetUri": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Required. Training dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.",
-               "title": "Trainingdataseturi"
-            },
-            "tuningMode": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/TuningMode"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Tuning mode."
-            },
-            "validationDatasetUri": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Validation dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.",
-               "title": "Validationdataseturi"
-            }
-         },
-         "title": "SupervisedTuningSpec",
-         "type": "object"
-      },
-      "TunedModel": {
-         "additionalProperties": false,
-         "properties": {
-            "model": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. The resource name of the TunedModel. Format: `projects/{project}/locations/{location}/models/{model}@{version_id}` When tuning from a base model, the version_id will be 1. For continuous tuning, the version id will be incremented by 1 from the last version id in the parent model. E.g., `projects/{project}/locations/{location}/models/{model}@{last_version_id + 1}`",
-               "title": "Model"
-            },
-            "endpoint": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. A resource name of an Endpoint. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`.",
-               "title": "Endpoint"
-            },
-            "checkpoints": {
-               "anyOf": [
-                  {
-                     "items": {
-                        "$ref": "#/$defs/TunedModelCheckpoint"
-                     },
-                     "type": "array"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The checkpoints associated with this TunedModel.\n      This field is only populated for tuning jobs that enable intermediate\n      checkpoints.",
-               "title": "Checkpoints"
-            }
-         },
-         "title": "TunedModel",
-         "type": "object"
-      },
-      "TunedModelCheckpoint": {
-         "additionalProperties": false,
-         "description": "TunedModelCheckpoint for the Tuned Model of a Tuning Job.",
-         "properties": {
-            "checkpointId": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The ID of the checkpoint.\n      ",
-               "title": "Checkpointid"
-            },
-            "epoch": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The epoch of the checkpoint.\n      ",
-               "title": "Epoch"
-            },
-            "step": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The step of the checkpoint.\n      ",
-               "title": "Step"
-            },
-            "endpoint": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The Endpoint resource name that the checkpoint is deployed to.\n      Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`.\n      ",
-               "title": "Endpoint"
-            }
-         },
-         "title": "TunedModelCheckpoint",
-         "type": "object"
-      },
-      "TuningDataStats": {
-         "additionalProperties": false,
-         "description": "The tuning data statistic values for TuningJob.",
-         "properties": {
-            "distillationDataStats": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/DistillationDataStats"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Statistics for distillation."
-            },
-            "preferenceOptimizationDataStats": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/PreferenceOptimizationDataStats"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Statistics for preference optimization."
-            },
-            "supervisedTuningDataStats": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/SupervisedTuningDataStats"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The SFT Tuning data stats."
-            }
-         },
-         "title": "TuningDataStats",
-         "type": "object"
-      },
-      "TuningJob": {
-         "additionalProperties": false,
-         "description": "A tuning job.",
-         "properties": {
-            "sdkHttpResponse": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/HttpResponse"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Used to retain the full HTTP response."
-            },
-            "name": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Identifier. Resource name of a TuningJob. Format: `projects/{project}/locations/{location}/tuningJobs/{tuning_job}`",
-               "title": "Name"
-            },
-            "state": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/JobState"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. The detailed state of the job."
-            },
-            "createTime": {
-               "anyOf": [
-                  {
-                     "format": "date-time",
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Time when the TuningJob was created.",
-               "title": "Createtime"
-            },
-            "startTime": {
-               "anyOf": [
-                  {
-                     "format": "date-time",
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Time when the TuningJob for the first time entered the `JOB_STATE_RUNNING` state.",
-               "title": "Starttime"
-            },
-            "endTime": {
-               "anyOf": [
-                  {
-                     "format": "date-time",
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Time when the TuningJob entered any of the following JobStates: `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED`, `JOB_STATE_CANCELLED`, `JOB_STATE_EXPIRED`.",
-               "title": "Endtime"
-            },
-            "updateTime": {
-               "anyOf": [
-                  {
-                     "format": "date-time",
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Time when the TuningJob was most recently updated.",
-               "title": "Updatetime"
-            },
-            "error": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/GoogleRpcStatus"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Only populated when job's state is `JOB_STATE_FAILED` or `JOB_STATE_CANCELLED`."
-            },
-            "description": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. The description of the TuningJob.",
-               "title": "Description"
-            },
-            "baseModel": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The base model that is being tuned. See [Supported models](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/tuning#supported_models).",
-               "title": "Basemodel"
-            },
-            "tunedModel": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/TunedModel"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. The tuned model resources associated with this TuningJob."
-            },
-            "preTunedModel": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/PreTunedModel"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The pre-tuned model for continuous tuning."
-            },
-            "supervisedTuningSpec": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/SupervisedTuningSpec"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Tuning Spec for Supervised Fine Tuning."
-            },
-            "tuningDataStats": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/TuningDataStats"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. The tuning data statistics associated with this TuningJob."
-            },
-            "encryptionSpec": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/EncryptionSpec"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Customer-managed encryption key options for a TuningJob. If this is set, then all resources created by the TuningJob will be encrypted with the provided encryption key."
-            },
-            "partnerModelTuningSpec": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/PartnerModelTuningSpec"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Tuning Spec for open sourced and third party Partner models."
-            },
-            "customBaseModel": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. The user-provided path to custom model weights. Set this field to tune a custom model. The path must be a Cloud Storage directory that contains the model weights in .safetensors format along with associated model metadata files. If this field is set, the base_model field must still be set to indicate which base model the custom model is derived from. This feature is only available for open source models.",
-               "title": "Custombasemodel"
-            },
-            "distillationSpec": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/DistillationSpec"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Tuning Spec for Distillation."
-            },
-            "experiment": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. The Experiment associated with this TuningJob.",
-               "title": "Experiment"
-            },
-            "labels": {
-               "anyOf": [
-                  {
-                     "additionalProperties": {
-                        "type": "string"
-                     },
-                     "type": "object"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. The labels with user-defined metadata to organize TuningJob and generated resources such as Model and Endpoint. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.",
-               "title": "Labels"
-            },
-            "outputUri": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Cloud Storage path to the directory where tuning job outputs are written to. This field is only available and required for open source models.",
-               "title": "Outputuri"
-            },
-            "pipelineJob": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. The resource name of the PipelineJob associated with the TuningJob. Format: `projects/{project}/locations/{location}/pipelineJobs/{pipeline_job}`.",
-               "title": "Pipelinejob"
-            },
-            "preferenceOptimizationSpec": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/PreferenceOptimizationSpec"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Tuning Spec for Preference Optimization."
-            },
-            "satisfiesPzi": {
-               "anyOf": [
-                  {
-                     "type": "boolean"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Reserved for future use.",
-               "title": "Satisfiespzi"
-            },
-            "satisfiesPzs": {
-               "anyOf": [
-                  {
-                     "type": "boolean"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Output only. Reserved for future use.",
-               "title": "Satisfiespzs"
-            },
-            "serviceAccount": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The service account that the tuningJob workload runs as. If not specified, the Vertex AI Secure Fine-Tuned Service Agent in the project will be used. See https://cloud.google.com/iam/docs/service-agents#vertex-ai-secure-fine-tuning-service-agent Users starting the pipeline must have the `iam.serviceAccounts.actAs` permission on this service account.",
-               "title": "Serviceaccount"
-            },
-            "tunedModelDisplayName": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. The display name of the TunedModel. The name can be up to 128 characters long and can consist of any UTF-8 characters.",
-               "title": "Tunedmodeldisplayname"
-            },
-            "veoTuningSpec": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/VeoTuningSpec"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Tuning Spec for Veo Tuning."
-            }
-         },
-         "title": "TuningJob",
-         "type": "object"
-      },
-      "TuningMode": {
-         "description": "Tuning mode.",
-         "enum": [
-            "TUNING_MODE_UNSPECIFIED",
-            "TUNING_MODE_FULL",
-            "TUNING_MODE_PEFT_ADAPTER"
-         ],
-         "title": "TuningMode",
-         "type": "string"
-      },
-      "TuningTask": {
-         "description": "Optional. The tuning task. Either I2V or T2V.",
-         "enum": [
-            "TUNING_TASK_UNSPECIFIED",
-            "TUNING_TASK_I2V",
-            "TUNING_TASK_T2V"
-         ],
-         "title": "TuningTask",
-         "type": "string"
-      },
-      "VeoHyperParameters": {
-         "additionalProperties": false,
-         "description": "Hyperparameters for Veo.",
-         "properties": {
-            "epochCount": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Number of complete passes the model makes over the entire training dataset during training.",
-               "title": "Epochcount"
-            },
-            "learningRateMultiplier": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Multiplier for adjusting the default learning rate.",
-               "title": "Learningratemultiplier"
-            },
-            "tuningTask": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/TuningTask"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. The tuning task. Either I2V or T2V."
-            }
-         },
-         "title": "VeoHyperParameters",
-         "type": "object"
-      },
-      "VeoTuningSpec": {
-         "additionalProperties": false,
-         "description": "Tuning Spec for Veo Model Tuning.",
-         "properties": {
-            "hyperParameters": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/VeoHyperParameters"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Hyperparameters for Veo."
-            },
-            "trainingDatasetUri": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Required. Training dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.",
-               "title": "Trainingdataseturi"
-            },
-            "validationDatasetUri": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Validation dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.",
-               "title": "Validationdataseturi"
-            }
-         },
-         "title": "VeoTuningSpec",
-         "type": "object"
-      },
-      "VideoMetadata": {
-         "additionalProperties": false,
-         "description": "Describes how the video in the Part should be used by the model.",
-         "properties": {
-            "fps": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The frame rate of the video sent to the model. If not specified, the\n        default value will be 1.0. The fps range is (0.0, 24.0].",
-               "title": "Fps"
-            },
-            "endOffset": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. The end offset of the video.",
-               "title": "Endoffset"
-            },
-            "startOffset": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. The start offset of the video.",
-               "title": "Startoffset"
-            }
-         },
-         "title": "VideoMetadata",
-         "type": "object"
-      }
-   },
-   "additionalProperties": false
-}
-
    -
    -

    -
    Fields:
    -
    -
    -
    -
    -
    -field next_page_token: Optional[str] = None (alias 'nextPageToken')
    -

    A token to retrieve the next page of results. Pass to ListTuningJobsRequest.page_token to obtain that page.

    -
    - -
    -
    -field sdk_http_response: Optional[HttpResponse] = None (alias 'sdkHttpResponse')
    -

    Used to retain the full HTTP response.

    -
    - -
    -
    -field tuning_jobs: Optional[list[TuningJob]] = None (alias 'tuningJobs')
    -

    List of TuningJobs in the requested page.

    -
    - -
    - -
    -
    -class genai.types.ListTuningJobsResponseDict
    -

    Bases: TypedDict

    -

    Response for the list tuning jobs method.

    -
    -
    -next_page_token: Optional[str]
    -

    A token to retrieve the next page of results. Pass to ListTuningJobsRequest.page_token to obtain that page.

    -
    - -
    -
    -sdk_http_response: Optional[HttpResponseDict]
    -

    Used to retain the full HTTP response.

    -
    - -
    -
    -tuning_jobs: Optional[list[TuningJobDict]]
    -

    List of TuningJobs in the requested page.

    -
    - -
    - -
    -
    -pydantic model genai.types.LiveClientContent
    -

    Bases: BaseModel

    -

    Incremental update of the current conversation delivered from the client.

    -

    All the content here will unconditionally be appended to the conversation -history and used as part of the prompt to the model to generate content.

    -

    A message here will interrupt any current model generation.

    -

    Create a new model by parsing and validating input data from keyword arguments.

    -

    Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be -validated to form a valid model.

    -

    self is explicitly positional-only to allow self as a field name.

    -

    -Show JSON schema
    {
-   "title": "LiveClientContent",
-   "description": "Incremental update of the current conversation delivered from the client.\n\nAll the content here will unconditionally be appended to the conversation\nhistory and used as part of the prompt to the model to generate content.\n\nA message here will interrupt any current model generation.",
-   "type": "object",
-   "properties": {
-      "turns": {
-         "anyOf": [
-            {
-               "items": {
-                  "$ref": "#/$defs/Content"
-               },
-               "type": "array"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "The content appended to the current conversation with the model.\n\n      For single-turn queries, this is a single instance. For multi-turn\n      queries, this is a repeated field that contains conversation history and\n      latest request.\n      ",
-         "title": "Turns"
-      },
-      "turnComplete": {
-         "anyOf": [
-            {
-               "type": "boolean"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "If true, indicates that the server content generation should start with\n  the currently accumulated prompt. Otherwise, the server will await\n  additional messages before starting generation.",
-         "title": "Turncomplete"
+         "description": "The content appended to the current conversation with the model.\n\n      For single-turn queries, this is a single instance. For multi-turn\n      queries, this is a repeated field that contains conversation history and\n      latest request.\n      ",
+         "title": "Turns"
+      },
+      "turnComplete": {
+         "anyOf": [
+            {
+               "type": "boolean"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "If true, indicates that the server content generation should start with\n  the currently accumulated prompt. Otherwise, the server will await\n  additional messages before starting generation.",
+         "title": "Turncomplete"
       }
    },
    "$defs": {
@@ -91385,10 +89798,36 @@ 
    Submodules      "EnterpriseWebSearch": {
          "additionalProperties": false,
          "description": "Tool to search public web data, powered by Vertex AI Search and Sec4 compliance.",
-         "properties": {},
+         "properties": {
+            "excludeDomains": {
+               "anyOf": [
+                  {
+                     "items": {
+                        "type": "string"
+                     },
+                     "type": "array"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Optional. List of domains to be excluded from the search results. The default limit is 2000 domains.",
+               "title": "Excludedomains"
+            }
+         },
          "title": "EnterpriseWebSearch",
          "type": "object"
       },
+      "Environment": {
+         "description": "The environment being operated.",
+         "enum": [
+            "ENVIRONMENT_UNSPECIFIED",
+            "ENVIRONMENT_BROWSER"
+         ],
+         "title": "Environment",
+         "type": "string"
+      },
       "ExecutableCode": {
          "additionalProperties": false,
          "description": "Code generated by the model that is meant to be executed, and the result returned to the model.\n\nGenerated when using the [CodeExecution] tool, in which the code will be\nautomatically executed, and a corresponding [CodeExecutionResult] will also be\ngenerated.",
@@ -92539,6 +90978,22 @@ 
    Submodules               ],
                "default": null,
                "description": "Optional. Filter search results to a specific time range.\n      If customers set a start time, they must set an end time (and vice versa).\n      "
+            },
+            "excludeDomains": {
+               "anyOf": [
+                  {
+                     "items": {
+                        "type": "string"
+                     },
+                     "type": "array"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Optional. List of domains to be excluded from the search results.\n      The default limit is 2000 domains.",
+               "title": "Excludedomains"
             }
          },
          "title": "GoogleSearch",
@@ -93980,6 +92435,26 @@ 
    Submodules         "title": "ToolCodeExecution",
          "type": "object"
       },
+      "ToolComputerUse": {
+         "additionalProperties": false,
+         "description": "Tool to support computer use.",
+         "properties": {
+            "environment": {
+               "anyOf": [
+                  {
+                     "$ref": "#/$defs/Environment"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Required. The environment being operated."
+            }
+         },
+         "title": "ToolComputerUse",
+         "type": "object"
+      },
       "Type": {
          "description": "Optional. The type of the data.",
          "enum": [
@@ -94399,6 +92874,18 @@ 
    Submodules               "default": null,
                "description": "Optional. Tool to support URL context retrieval."
             },
+            "computerUse": {
+               "anyOf": [
+                  {
+                     "$ref": "#/$defs/ToolComputerUse"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Optional. Tool to support the model interacting directly with the\n      computer. If enabled, it automatically populates computer-use specific\n      Function Declarations."
+            },
             "codeExecution": {
                "anyOf": [
                   {
@@ -95513,10 +94000,36 @@ 
    Submodules      "EnterpriseWebSearch": {
          "additionalProperties": false,
          "description": "Tool to search public web data, powered by Vertex AI Search and Sec4 compliance.",
-         "properties": {},
+         "properties": {
+            "excludeDomains": {
+               "anyOf": [
+                  {
+                     "items": {
+                        "type": "string"
+                     },
+                     "type": "array"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Optional. List of domains to be excluded from the search results. The default limit is 2000 domains.",
+               "title": "Excludedomains"
+            }
+         },
          "title": "EnterpriseWebSearch",
          "type": "object"
       },
+      "Environment": {
+         "description": "The environment being operated.",
+         "enum": [
+            "ENVIRONMENT_UNSPECIFIED",
+            "ENVIRONMENT_BROWSER"
+         ],
+         "title": "Environment",
+         "type": "string"
+      },
       "ExecutableCode": {
          "additionalProperties": false,
          "description": "Code generated by the model that is meant to be executed, and the result returned to the model.\n\nGenerated when using the [CodeExecution] tool, in which the code will be\nautomatically executed, and a corresponding [CodeExecutionResult] will also be\ngenerated.",
@@ -96667,6 +95180,22 @@ 
    Submodules               ],
                "default": null,
                "description": "Optional. Filter search results to a specific time range.\n      If customers set a start time, they must set an end time (and vice versa).\n      "
+            },
+            "excludeDomains": {
+               "anyOf": [
+                  {
+                     "items": {
+                        "type": "string"
+                     },
+                     "type": "array"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Optional. List of domains to be excluded from the search results.\n      The default limit is 2000 domains.",
+               "title": "Excludedomains"
             }
          },
          "title": "GoogleSearch",
@@ -97795,6 +96324,26 @@ 
    Submodules         "title": "ToolCodeExecution",
          "type": "object"
       },
+      "ToolComputerUse": {
+         "additionalProperties": false,
+         "description": "Tool to support computer use.",
+         "properties": {
+            "environment": {
+               "anyOf": [
+                  {
+                     "$ref": "#/$defs/Environment"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Required. The environment being operated."
+            }
+         },
+         "title": "ToolComputerUse",
+         "type": "object"
+      },
       "Type": {
          "description": "Optional. The type of the data.",
          "enum": [
@@ -98214,6 +96763,18 @@ 
    Submodules               "default": null,
                "description": "Optional. Tool to support URL context retrieval."
             },
+            "computerUse": {
+               "anyOf": [
+                  {
+                     "$ref": "#/$defs/ToolComputerUse"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Optional. Tool to support the model interacting directly with the\n      computer. If enabled, it automatically populates computer-use specific\n      Function Declarations."
+            },
             "codeExecution": {
                "anyOf": [
                   {
@@ -99478,10 +98039,36 @@ 
    Submodules      "EnterpriseWebSearch": {
          "additionalProperties": false,
          "description": "Tool to search public web data, powered by Vertex AI Search and Sec4 compliance.",
-         "properties": {},
+         "properties": {
+            "excludeDomains": {
+               "anyOf": [
+                  {
+                     "items": {
+                        "type": "string"
+                     },
+                     "type": "array"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Optional. List of domains to be excluded from the search results. The default limit is 2000 domains.",
+               "title": "Excludedomains"
+            }
+         },
          "title": "EnterpriseWebSearch",
          "type": "object"
       },
+      "Environment": {
+         "description": "The environment being operated.",
+         "enum": [
+            "ENVIRONMENT_UNSPECIFIED",
+            "ENVIRONMENT_BROWSER"
+         ],
+         "title": "Environment",
+         "type": "string"
+      },
       "ExecutableCode": {
          "additionalProperties": false,
          "description": "Code generated by the model that is meant to be executed, and the result returned to the model.\n\nGenerated when using the [CodeExecution] tool, in which the code will be\nautomatically executed, and a corresponding [CodeExecutionResult] will also be\ngenerated.",
@@ -100632,6 +99219,22 @@ 
    Submodules               ],
                "default": null,
                "description": "Optional. Filter search results to a specific time range.\n      If customers set a start time, they must set an end time (and vice versa).\n      "
+            },
+            "excludeDomains": {
+               "anyOf": [
+                  {
+                     "items": {
+                        "type": "string"
+                     },
+                     "type": "array"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Optional. List of domains to be excluded from the search results.\n      The default limit is 2000 domains.",
+               "title": "Excludedomains"
             }
          },
          "title": "GoogleSearch",
@@ -102017,6 +100620,26 @@ 
    Submodules         "title": "ToolCodeExecution",
          "type": "object"
       },
+      "ToolComputerUse": {
+         "additionalProperties": false,
+         "description": "Tool to support computer use.",
+         "properties": {
+            "environment": {
+               "anyOf": [
+                  {
+                     "$ref": "#/$defs/Environment"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Required. The environment being operated."
+            }
+         },
+         "title": "ToolComputerUse",
+         "type": "object"
+      },
       "TurnCoverage": {
          "description": "Options about which input is included in the user's turn.",
          "enum": [
@@ -102446,6 +101069,18 @@ 
    Submodules               "default": null,
                "description": "Optional. Tool to support URL context retrieval."
             },
+            "computerUse": {
+               "anyOf": [
+                  {
+                     "$ref": "#/$defs/ToolComputerUse"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Optional. Tool to support the model interacting directly with the\n      computer. If enabled, it automatically populates computer-use specific\n      Function Declarations."
+            },
             "codeExecution": {
                "anyOf": [
                   {
@@ -103449,10 +102084,36 @@ 
    Submodules      "EnterpriseWebSearch": {
          "additionalProperties": false,
          "description": "Tool to search public web data, powered by Vertex AI Search and Sec4 compliance.",
-         "properties": {},
+         "properties": {
+            "excludeDomains": {
+               "anyOf": [
+                  {
+                     "items": {
+                        "type": "string"
+                     },
+                     "type": "array"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Optional. List of domains to be excluded from the search results. The default limit is 2000 domains.",
+               "title": "Excludedomains"
+            }
+         },
          "title": "EnterpriseWebSearch",
          "type": "object"
       },
+      "Environment": {
+         "description": "The environment being operated.",
+         "enum": [
+            "ENVIRONMENT_UNSPECIFIED",
+            "ENVIRONMENT_BROWSER"
+         ],
+         "title": "Environment",
+         "type": "string"
+      },
       "ExecutableCode": {
          "additionalProperties": false,
          "description": "Code generated by the model that is meant to be executed, and the result returned to the model.\n\nGenerated when using the [CodeExecution] tool, in which the code will be\nautomatically executed, and a corresponding [CodeExecutionResult] will also be\ngenerated.",
@@ -104603,6 +103264,22 @@ 
    Submodules               ],
                "default": null,
                "description": "Optional. Filter search results to a specific time range.\n      If customers set a start time, they must set an end time (and vice versa).\n      "
+            },
+            "excludeDomains": {
+               "anyOf": [
+                  {
+                     "items": {
+                        "type": "string"
+                     },
+                     "type": "array"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Optional. List of domains to be excluded from the search results.\n      The default limit is 2000 domains.",
+               "title": "Excludedomains"
             }
          },
          "title": "GoogleSearch",
@@ -106271,6 +104948,26 @@ 
    Submodules         "title": "ToolCodeExecution",
          "type": "object"
       },
+      "ToolComputerUse": {
+         "additionalProperties": false,
+         "description": "Tool to support computer use.",
+         "properties": {
+            "environment": {
+               "anyOf": [
+                  {
+                     "$ref": "#/$defs/Environment"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Required. The environment being operated."
+            }
+         },
+         "title": "ToolComputerUse",
+         "type": "object"
+      },
       "TurnCoverage": {
          "description": "Options about which input is included in the user's turn.",
          "enum": [
@@ -106700,6 +105397,18 @@ 
    Submodules               "default": null,
                "description": "Optional. Tool to support URL context retrieval."
             },
+            "computerUse": {
+               "anyOf": [
+                  {
+                     "$ref": "#/$defs/ToolComputerUse"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Optional. Tool to support the model interacting directly with the\n      computer. If enabled, it automatically populates computer-use specific\n      Function Declarations."
+            },
             "codeExecution": {
                "anyOf": [
                   {
@@ -107444,10 +106153,36 @@ 
    Submodules      "EnterpriseWebSearch": {
          "additionalProperties": false,
          "description": "Tool to search public web data, powered by Vertex AI Search and Sec4 compliance.",
-         "properties": {},
+         "properties": {
+            "excludeDomains": {
+               "anyOf": [
+                  {
+                     "items": {
+                        "type": "string"
+                     },
+                     "type": "array"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Optional. List of domains to be excluded from the search results. The default limit is 2000 domains.",
+               "title": "Excludedomains"
+            }
+         },
          "title": "EnterpriseWebSearch",
          "type": "object"
       },
+      "Environment": {
+         "description": "The environment being operated.",
+         "enum": [
+            "ENVIRONMENT_UNSPECIFIED",
+            "ENVIRONMENT_BROWSER"
+         ],
+         "title": "Environment",
+         "type": "string"
+      },
       "ExecutableCode": {
          "additionalProperties": false,
          "description": "Code generated by the model that is meant to be executed, and the result returned to the model.\n\nGenerated when using the [CodeExecution] tool, in which the code will be\nautomatically executed, and a corresponding [CodeExecutionResult] will also be\ngenerated.",
@@ -108598,6 +107333,22 @@ 
    Submodules               ],
                "default": null,
                "description": "Optional. Filter search results to a specific time range.\n      If customers set a start time, they must set an end time (and vice versa).\n      "
+            },
+            "excludeDomains": {
+               "anyOf": [
+                  {
+                     "items": {
+                        "type": "string"
+                     },
+                     "type": "array"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Optional. List of domains to be excluded from the search results.\n      The default limit is 2000 domains.",
+               "title": "Excludedomains"
             }
          },
          "title": "GoogleSearch",
@@ -110266,6 +109017,26 @@ 
    Submodules         "title": "ToolCodeExecution",
          "type": "object"
       },
+      "ToolComputerUse": {
+         "additionalProperties": false,
+         "description": "Tool to support computer use.",
+         "properties": {
+            "environment": {
+               "anyOf": [
+                  {
+                     "$ref": "#/$defs/Environment"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Required. The environment being operated."
+            }
+         },
+         "title": "ToolComputerUse",
+         "type": "object"
+      },
       "TurnCoverage": {
          "description": "Options about which input is included in the user's turn.",
          "enum": [
@@ -110695,6 +109466,18 @@ 
    Submodules               "default": null,
                "description": "Optional. Tool to support URL context retrieval."
             },
+            "computerUse": {
+               "anyOf": [
+                  {
+                     "$ref": "#/$defs/ToolComputerUse"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Optional. Tool to support the model interacting directly with the\n      computer. If enabled, it automatically populates computer-use specific\n      Function Declarations."
+            },
             "codeExecution": {
                "anyOf": [
                   {
@@ -114604,6 +113387,19 @@ 
    Submodules         "additionalProperties": false,
          "description": "Chunk from context retrieved by the retrieval tools.",
          "properties": {
+            "documentName": {
+               "anyOf": [
+                  {
+                     "type": "string"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Output only. The full document name for the referenced Vertex AI Search document.",
+               "title": "Documentname"
+            },
             "ragChunk": {
                "anyOf": [
                   {
@@ -116266,6 +115062,19 @@ 
    Submodules         "additionalProperties": false,
          "description": "Chunk from context retrieved by the retrieval tools.",
          "properties": {
+            "documentName": {
+               "anyOf": [
+                  {
+                     "type": "string"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Output only. The full document name for the referenced Vertex AI Search document.",
+               "title": "Documentname"
+            },
             "ragChunk": {
                "anyOf": [
                   {
@@ -117521,13 +116330,13 @@ 
    Submodules
 
    
 field tool_call: Optional[LiveServerToolCall] = None (alias 'toolCall')
    
-
    Request for the client to execute the function_calls and return the responses with the matching `id`s.
    
+
    Request for the client to execute the function_calls and return the responses with the matching `id`s.
    field tool_call_cancellation: Optional[LiveServerToolCallCancellation] = None (alias 'toolCallCancellation')
    -

    Notification for the client that a previously issued ToolCallMessage with the specified `id`s should have been not executed and should be cancelled.

    +

    Notification for the client that a previously issued ToolCallMessage with the specified `id`s should have been not executed and should be cancelled.

    @@ -117582,13 +116391,13 @@

    Submodules
    tool_call: Optional[LiveServerToolCallDict]
    -

    Request for the client to execute the function_calls and return the responses with the matching `id`s.

    +

    Request for the client to execute the function_calls and return the responses with the matching `id`s.

    tool_call_cancellation: Optional[LiveServerToolCallCancellationDict]
    -

    Notification for the client that a previously issued ToolCallMessage with the specified `id`s should have been not executed and should be cancelled.

    +

    Notification for the client that a previously issued ToolCallMessage with the specified `id`s should have been not executed and should be cancelled.

    @@ -117679,7 +116488,7 @@

    Submodules
    field new_handle: Optional[str] = None (alias 'newHandle')
    -

    New handle that represents state that can be resumed. Empty if `resumable`=false.

    +

    New handle that represents state that can be resumed. Empty if `resumable`=false.

    @@ -117707,7 +116516,7 @@

    Submodules
    new_handle: Optional[str]
    -

    New handle that represents state that can be resumed. Empty if `resumable`=false.

    +

    New handle that represents state that can be resumed. Empty if `resumable`=false.

    @@ -117783,7 +116592,7 @@

    Submodules pydantic model genai.types.LiveServerToolCall

    Bases: BaseModel

    -

    Request for the client to execute the function_calls and return the responses with the matching `id`s.

    +

    Request for the client to execute the function_calls and return the responses with the matching `id`s.

    Create a new model by parsing and validating input data from keyword arguments.

    Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

    @@ -117883,7 +116692,7 @@

    Submodules pydantic model genai.types.LiveServerToolCallCancellation

    Bases: BaseModel

    -

    Notification for the client that a previously issued ToolCallMessage with the specified `id`s should have been not executed and should be cancelled.

    +

    Notification for the client that a previously issued ToolCallMessage with the specified `id`s should have been not executed and should be cancelled.

    If there were side-effects to those tool calls, clients may attempt to undo the tool calls. This message occurs only in cases where the clients interrupt server turns.

    @@ -117937,7 +116746,7 @@

    Submodules class genai.types.LiveServerToolCallCancellationDict

    Bases: TypedDict

    -

    Notification for the client that a previously issued ToolCallMessage with the specified `id`s should have been not executed and should be cancelled.

    +

    Notification for the client that a previously issued ToolCallMessage with the specified `id`s should have been not executed and should be cancelled.

    If there were side-effects to those tool calls, clients may attempt to undo the tool calls. This message occurs only in cases where the clients interrupt server turns.

    @@ -117953,7 +116762,7 @@

    Submodules class genai.types.LiveServerToolCallDict

    Bases: TypedDict

    -

    Request for the client to execute the function_calls and return the responses with the matching `id`s.

    +

    Request for the client to execute the function_calls and return the responses with the matching `id`s.

    function_calls: Optional[list[FunctionCallDict]]
    @@ -118914,6 +117723,297 @@

    Submodules +
    +pydantic model genai.types.Metric
    +

    Bases: BaseModel

    +

    The metric used for evaluation.

    +

    Create a new model by parsing and validating input data from keyword arguments.

    +

    Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be +validated to form a valid model.

    +

    self is explicitly positional-only to allow self as a field name.

    +

    +Show JSON schema
    {
+   "title": "Metric",
+   "type": "object",
+   "properties": {
+      "name": {
+         "anyOf": [
+            {
+               "type": "string"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "The name of the metric.",
+         "title": "Name"
+      },
+      "customFunction": {
+         "default": null,
+         "title": "Customfunction"
+      },
+      "promptTemplate": {
+         "anyOf": [
+            {
+               "type": "string"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "The prompt template for the metric.",
+         "title": "Prompttemplate"
+      },
+      "judgeModelSystemInstruction": {
+         "anyOf": [
+            {
+               "type": "string"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "The system instruction for the judge model.",
+         "title": "Judgemodelsysteminstruction"
+      },
+      "returnRawOutput": {
+         "anyOf": [
+            {
+               "type": "boolean"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "Whether to return the raw output from the judge model.",
+         "title": "Returnrawoutput"
+      },
+      "parseAndReduceFn": {
+         "default": null,
+         "title": "Parseandreducefn"
+      },
+      "aggregateSummaryFn": {
+         "default": null,
+         "title": "Aggregatesummaryfn"
+      }
+   },
+   "additionalProperties": true
+}
+
    +
    +

    +
    Fields:
    +
    +
    +
    Validators:
    +
    +
    +
    +
    +
    +field aggregate_summary_fn: Optional[Callable[..., Any]] = None (alias 'aggregateSummaryFn')
    +

    The aggregate summary function for the judge model.

    +
    +
    Validated by:
    +
    +
    +
    +
    + +
    +
    +field custom_function: Optional[Callable[..., Any]] = None (alias 'customFunction')
    +

    The custom function that defines the end-to-end logic for metric computation.

    +
    +
    Validated by:
    +
    +
    +
    +
    + +
    +
    +field judge_model_system_instruction: Optional[str] = None (alias 'judgeModelSystemInstruction')
    +

    The system instruction for the judge model.

    +
    +
    Validated by:
    +
    +
    +
    +
    + +
    +
    +field name: Optional[str] = None
    +

    The name of the metric.

    +
    +
    Validated by:
    +
    +
    +
    +
    + +
    +
    +field parse_and_reduce_fn: Optional[Callable[..., Any]] = None (alias 'parseAndReduceFn')
    +

    The parse and reduce function for the judge model.

    +
    +
    Validated by:
    +
    +
    +
    +
    + +
    +
    +field prompt_template: Optional[str] = None (alias 'promptTemplate')
    +

    The prompt template for the metric.

    +
    +
    Validated by:
    +
    +
    +
    +
    + +
    +
    +field return_raw_output: Optional[bool] = None (alias 'returnRawOutput')
    +

    Whether to return the raw output from the judge model.

    +
    +
    Validated by:
    +
    +
    +
    +
    + +
    +
    +validator validate_name  »  all fields
    +
    +
    Return type:
    +

    Metric

    +
    +
    +
    + +
    +
    +model_post_init(context, /)
    +

    This function is meant to behave like a BaseModel method to initialise private attributes.

    +

    It takes context as an argument since that’s what pydantic-core passes when calling it.

    +
    +
    Return type:
    +

    None

    +
    +
    Parameters:
    +
      +

    • self – The BaseModel instance.

      • +

    • context – The context.

      • +
    +
    +
    +
    + +
    +
    +to_yaml_file(file_path, version=None)
    +

    Dumps the metric object to a YAML file.

    +
    +
    Return type:
    +

    None

    +
    +
    Parameters:
    +
      +

    • file_path – The path to the YAML file.

      • +

    • version – Optional version string to include in the YAML output.

      • +
    +
    +
    Raises:
    +

    ImportError – If the pyyaml library is not installed.

    +
    +
    +
    + +

    + +
    +
    +class genai.types.MetricDict
    +

    Bases: TypedDict

    +

    The metric used for evaluation.

    +
    +
    +aggregate_summary_fn: Optional[Callable[..., Any]]
    +

    The aggregate summary function for the judge model.

    +
    + +
    +
    +custom_function: Optional[Callable[..., Any]]
    +

    The custom function that defines the end-to-end logic for metric computation.

    +
    + +
    +
    +judge_model_system_instruction: Optional[str]
    +

    The system instruction for the judge model.

    +
    + +
    +
    +name: Optional[str]
    +

    The name of the metric.

    +
    + +
    +
    +parse_and_reduce_fn: Optional[Callable[..., Any]]
    +

    The parse and reduce function for the judge model.

    +
    + +
    +
    +prompt_template: Optional[str]
    +

    The prompt template for the metric.

    +
    + +
    +
    +return_raw_output: Optional[bool]
    +

    Whether to return the raw output from the judge model.

    +
    + +
    +
    class genai.types.Modality(*values)
    @@ -120445,6 +119545,276 @@

    Submodules +
    +pydantic model genai.types.OutputConfig
    +

    Bases: BaseModel

    +

    Config for evaluation output.

    +

    Create a new model by parsing and validating input data from keyword arguments.

    +

    Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be +validated to form a valid model.

    +

    self is explicitly positional-only to allow self as a field name.

    +

    +Show JSON schema
    {
+   "title": "OutputConfig",
+   "description": "Config for evaluation output.",
+   "type": "object",
+   "properties": {
+      "gcsDestination": {
+         "anyOf": [
+            {
+               "$ref": "#/$defs/GcsDestination"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "Cloud storage destination for evaluation output."
+      }
+   },
+   "$defs": {
+      "GcsDestination": {
+         "additionalProperties": false,
+         "description": "The Google Cloud Storage location where the output is to be written to.",
+         "properties": {
+            "outputUriPrefix": {
+               "anyOf": [
+                  {
+                     "type": "string"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Required. Google Cloud Storage URI to output directory. If the uri doesn't end with '/', a '/' will be automatically appended. The directory is created if it doesn't exist.",
+               "title": "Outputuriprefix"
+            }
+         },
+         "title": "GcsDestination",
+         "type": "object"
+      }
+   },
+   "additionalProperties": false
+}
+
    +
    +

    +
    Fields:
    +
    +
    +
    +
    +
    +field gcs_destination: Optional[GcsDestination] = None (alias 'gcsDestination')
    +

    Cloud storage destination for evaluation output.

    +
    + +

    + +
    +
    +class genai.types.OutputConfigDict
    +

    Bases: TypedDict

    +

    Config for evaluation output.

    +
    +
    +gcs_destination: Optional[GcsDestinationDict]
    +

    Cloud storage destination for evaluation output.

    +
    + +
    + +
    +
    +pydantic model genai.types.PairwiseMetricSpec
    +

    Bases: BaseModel

    +

    Spec for pairwise metric.

    +

    Create a new model by parsing and validating input data from keyword arguments.

    +

    Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be +validated to form a valid model.

    +

    self is explicitly positional-only to allow self as a field name.

    +

    +Show JSON schema
    {
+   "title": "PairwiseMetricSpec",
+   "description": "Spec for pairwise metric.",
+   "type": "object",
+   "properties": {
+      "metricPromptTemplate": {
+         "anyOf": [
+            {
+               "type": "string"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "Required. Metric prompt template for pairwise metric.",
+         "title": "Metricprompttemplate"
+      },
+      "baselineResponseFieldName": {
+         "anyOf": [
+            {
+               "type": "string"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "Optional. The field name of the baseline response.",
+         "title": "Baselineresponsefieldname"
+      },
+      "candidateResponseFieldName": {
+         "anyOf": [
+            {
+               "type": "string"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "Optional. The field name of the candidate response.",
+         "title": "Candidateresponsefieldname"
+      },
+      "customOutputFormatConfig": {
+         "anyOf": [
+            {
+               "$ref": "#/$defs/CustomOutputFormatConfig"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "Optional. CustomOutputFormatConfig allows customization of metric output. When this config is set, the default output is replaced with the raw output string. If a custom format is chosen, the `pairwise_choice` and `explanation` fields in the corresponding metric result will be empty."
+      },
+      "systemInstruction": {
+         "anyOf": [
+            {
+               "type": "string"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "Optional. System instructions for pairwise metric.",
+         "title": "Systeminstruction"
+      }
+   },
+   "$defs": {
+      "CustomOutputFormatConfig": {
+         "additionalProperties": false,
+         "description": "Config for custom output format.",
+         "properties": {
+            "returnRawOutput": {
+               "anyOf": [
+                  {
+                     "type": "boolean"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Optional. Whether to return raw output.",
+               "title": "Returnrawoutput"
+            }
+         },
+         "title": "CustomOutputFormatConfig",
+         "type": "object"
+      }
+   },
+   "additionalProperties": false
+}
+
    +
    +

    +
    Fields:
    +
    +
    +
    +
    +
    +field baseline_response_field_name: Optional[str] = None (alias 'baselineResponseFieldName')
    +

    Optional. The field name of the baseline response.

    +
    + +
    +
    +field candidate_response_field_name: Optional[str] = None (alias 'candidateResponseFieldName')
    +

    Optional. The field name of the candidate response.

    +
    + +
    +
    +field custom_output_format_config: Optional[CustomOutputFormatConfig] = None (alias 'customOutputFormatConfig')
    +

    Optional. CustomOutputFormatConfig allows customization of metric output. When this config is set, the default output is replaced with the raw output string. If a custom format is chosen, the pairwise_choice and explanation fields in the corresponding metric result will be empty.

    +
    + +
    +
    +field metric_prompt_template: Optional[str] = None (alias 'metricPromptTemplate')
    +

    Required. Metric prompt template for pairwise metric.

    +
    + +
    +
    +field system_instruction: Optional[str] = None (alias 'systemInstruction')
    +

    Optional. System instructions for pairwise metric.

    +
    + +
    + +
    +
    +class genai.types.PairwiseMetricSpecDict
    +

    Bases: TypedDict

    +

    Spec for pairwise metric.

    +
    +
    +baseline_response_field_name: Optional[str]
    +

    Optional. The field name of the baseline response.

    +
    + +
    +
    +candidate_response_field_name: Optional[str]
    +

    Optional. The field name of the candidate response.

    +
    + +
    +
    +custom_output_format_config: Optional[CustomOutputFormatConfigDict]
    +

    Optional. CustomOutputFormatConfig allows customization of metric output. When this config is set, the default output is replaced with the raw output string. If a custom format is chosen, the pairwise_choice and explanation fields in the corresponding metric result will be empty.

    +
    + +
    +
    +metric_prompt_template: Optional[str]
    +

    Required. Metric prompt template for pairwise metric.

    +
    + +
    +
    +system_instruction: Optional[str]
    +

    Optional. System instructions for pairwise metric.

    +
    + +
    +
    pydantic model genai.types.Part
    @@ -121316,6 +120686,148 @@

    Submodules +
    +pydantic model genai.types.PointwiseMetricSpec
    +

    Bases: BaseModel

    +

    Spec for pointwise metric.

    +

    Create a new model by parsing and validating input data from keyword arguments.

    +

    Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be +validated to form a valid model.

    +

    self is explicitly positional-only to allow self as a field name.

    +

    +Show JSON schema
    {
+   "title": "PointwiseMetricSpec",
+   "description": "Spec for pointwise metric.",
+   "type": "object",
+   "properties": {
+      "metricPromptTemplate": {
+         "anyOf": [
+            {
+               "type": "string"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "Required. Metric prompt template for pointwise metric.",
+         "title": "Metricprompttemplate"
+      },
+      "customOutputFormatConfig": {
+         "anyOf": [
+            {
+               "$ref": "#/$defs/CustomOutputFormatConfig"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "Optional. CustomOutputFormatConfig allows customization of metric output. By default, metrics return a score and explanation. When this config is set, the default output is replaced with either: - The raw output string. - A parsed output based on a user-defined schema. If a custom format is chosen, the `score` and `explanation` fields in the corresponding metric result will be empty."
+      },
+      "systemInstruction": {
+         "anyOf": [
+            {
+               "type": "string"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "Optional. System instructions for pointwise metric.",
+         "title": "Systeminstruction"
+      }
+   },
+   "$defs": {
+      "CustomOutputFormatConfig": {
+         "additionalProperties": false,
+         "description": "Config for custom output format.",
+         "properties": {
+            "returnRawOutput": {
+               "anyOf": [
+                  {
+                     "type": "boolean"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Optional. Whether to return raw output.",
+               "title": "Returnrawoutput"
+            }
+         },
+         "title": "CustomOutputFormatConfig",
+         "type": "object"
+      }
+   },
+   "additionalProperties": false
+}
+
    +
    +

    +
    Fields:
    +
    +
    +
    +
    +
    +field custom_output_format_config: Optional[CustomOutputFormatConfig] = None (alias 'customOutputFormatConfig')
    +

    Optional. CustomOutputFormatConfig allows customization of metric output. By default, metrics return a score and explanation. When this config is set, the default output is replaced with either: - The raw output string. - A parsed output based on a user-defined schema. If a custom format is chosen, the score and explanation fields in the corresponding metric result will be empty.

    +
    + +
    +
    +field metric_prompt_template: Optional[str] = None (alias 'metricPromptTemplate')
    +

    Required. Metric prompt template for pointwise metric.

    +
    + +
    +
    +field system_instruction: Optional[str] = None (alias 'systemInstruction')
    +

    Optional. System instructions for pointwise metric.

    +
    + +

    + +
    +
    +class genai.types.PointwiseMetricSpecDict
    +

    Bases: TypedDict

    +

    Spec for pointwise metric.

    +
    +
    +custom_output_format_config: Optional[CustomOutputFormatConfigDict]
    +
      +

    • The raw output string. - A parsed output based on a user-defined schema. If a custom format is chosen, the score and explanation fields in the corresponding metric result will be empty.

      • +
    +
    +
    Type:
    +

    Optional. CustomOutputFormatConfig allows customization of metric output. By default, metrics return a score and explanation. When this config is set, the default output is replaced with either

    +
    +
    +
    + +
    +
    +metric_prompt_template: Optional[str]
    +

    Required. Metric prompt template for pointwise metric.

    +
    + +
    +
    +system_instruction: Optional[str]
    +

    Optional. System instructions for pointwise metric.

    +
    + +
    +
    pydantic model genai.types.PreTunedModel
    @@ -122504,347 +122016,6 @@

    Submodules -
    -pydantic model genai.types.PreferenceOptimizationHyperParameters
    -

    Bases: BaseModel

    -

    Hyperparameters for Preference Optimization.

    -

    Create a new model by parsing and validating input data from keyword arguments.

    -

    Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be -validated to form a valid model.

    -

    self is explicitly positional-only to allow self as a field name.

    -

    -Show JSON schema
    {
-   "title": "PreferenceOptimizationHyperParameters",
-   "description": "Hyperparameters for Preference Optimization.",
-   "type": "object",
-   "properties": {
-      "adapterSize": {
-         "anyOf": [
-            {
-               "$ref": "#/$defs/AdapterSize"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "Optional. Adapter size for preference optimization."
-      },
-      "beta": {
-         "anyOf": [
-            {
-               "type": "number"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "Optional. Weight for KL Divergence regularization.",
-         "title": "Beta"
-      },
-      "epochCount": {
-         "anyOf": [
-            {
-               "type": "integer"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "Optional. Number of complete passes the model makes over the entire training dataset during training.",
-         "title": "Epochcount"
-      },
-      "learningRateMultiplier": {
-         "anyOf": [
-            {
-               "type": "number"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "Optional. Multiplier for adjusting the default learning rate.",
-         "title": "Learningratemultiplier"
-      }
-   },
-   "$defs": {
-      "AdapterSize": {
-         "description": "Optional. Adapter size for tuning.",
-         "enum": [
-            "ADAPTER_SIZE_UNSPECIFIED",
-            "ADAPTER_SIZE_ONE",
-            "ADAPTER_SIZE_TWO",
-            "ADAPTER_SIZE_FOUR",
-            "ADAPTER_SIZE_EIGHT",
-            "ADAPTER_SIZE_SIXTEEN",
-            "ADAPTER_SIZE_THIRTY_TWO"
-         ],
-         "title": "AdapterSize",
-         "type": "string"
-      }
-   },
-   "additionalProperties": false
-}
-
    -
    -

    -
    Fields:
    -
    -
    -
    -
    -
    -field adapter_size: Optional[AdapterSize] = None (alias 'adapterSize')
    -

    Optional. Adapter size for preference optimization.

    -
    - -
    -
    -field beta: Optional[float] = None
    -

    Optional. Weight for KL Divergence regularization.

    -
    - -
    -
    -field epoch_count: Optional[int] = None (alias 'epochCount')
    -

    Optional. Number of complete passes the model makes over the entire training dataset during training.

    -
    - -
    -
    -field learning_rate_multiplier: Optional[float] = None (alias 'learningRateMultiplier')
    -

    Optional. Multiplier for adjusting the default learning rate.

    -
    - -

    - -
    -
    -class genai.types.PreferenceOptimizationHyperParametersDict
    -

    Bases: TypedDict

    -

    Hyperparameters for Preference Optimization.

    -
    -
    -adapter_size: Optional[AdapterSize]
    -

    Optional. Adapter size for preference optimization.

    -
    - -
    -
    -beta: Optional[float]
    -

    Optional. Weight for KL Divergence regularization.

    -
    - -
    -
    -epoch_count: Optional[int]
    -

    Optional. Number of complete passes the model makes over the entire training dataset during training.

    -
    - -
    -
    -learning_rate_multiplier: Optional[float]
    -

    Optional. Multiplier for adjusting the default learning rate.

    -
    - -
    - -
    -
    -pydantic model genai.types.PreferenceOptimizationSpec
    -

    Bases: BaseModel

    -

    Tuning Spec for Preference Optimization.

    -

    Create a new model by parsing and validating input data from keyword arguments.

    -

    Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be -validated to form a valid model.

    -

    self is explicitly positional-only to allow self as a field name.

    -

    -Show JSON schema
    {
-   "title": "PreferenceOptimizationSpec",
-   "description": "Tuning Spec for Preference Optimization.",
-   "type": "object",
-   "properties": {
-      "hyperParameters": {
-         "anyOf": [
-            {
-               "$ref": "#/$defs/PreferenceOptimizationHyperParameters"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "Optional. Hyperparameters for Preference Optimization."
-      },
-      "trainingDatasetUri": {
-         "anyOf": [
-            {
-               "type": "string"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "Required. Cloud Storage path to file containing training dataset for preference optimization tuning. The dataset must be formatted as a JSONL file.",
-         "title": "Trainingdataseturi"
-      },
-      "validationDatasetUri": {
-         "anyOf": [
-            {
-               "type": "string"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "Optional. Cloud Storage path to file containing validation dataset for preference optimization tuning. The dataset must be formatted as a JSONL file.",
-         "title": "Validationdataseturi"
-      }
-   },
-   "$defs": {
-      "AdapterSize": {
-         "description": "Optional. Adapter size for tuning.",
-         "enum": [
-            "ADAPTER_SIZE_UNSPECIFIED",
-            "ADAPTER_SIZE_ONE",
-            "ADAPTER_SIZE_TWO",
-            "ADAPTER_SIZE_FOUR",
-            "ADAPTER_SIZE_EIGHT",
-            "ADAPTER_SIZE_SIXTEEN",
-            "ADAPTER_SIZE_THIRTY_TWO"
-         ],
-         "title": "AdapterSize",
-         "type": "string"
-      },
-      "PreferenceOptimizationHyperParameters": {
-         "additionalProperties": false,
-         "description": "Hyperparameters for Preference Optimization.",
-         "properties": {
-            "adapterSize": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/AdapterSize"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Adapter size for preference optimization."
-            },
-            "beta": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Weight for KL Divergence regularization.",
-               "title": "Beta"
-            },
-            "epochCount": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Number of complete passes the model makes over the entire training dataset during training.",
-               "title": "Epochcount"
-            },
-            "learningRateMultiplier": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Multiplier for adjusting the default learning rate.",
-               "title": "Learningratemultiplier"
-            }
-         },
-         "title": "PreferenceOptimizationHyperParameters",
-         "type": "object"
-      }
-   },
-   "additionalProperties": false
-}
-
    -
    -

    -
    Fields:
    -
    -
    -
    -
    -
    -field hyper_parameters: Optional[PreferenceOptimizationHyperParameters] = None (alias 'hyperParameters')
    -

    Optional. Hyperparameters for Preference Optimization.

    -
    - -
    -
    -field training_dataset_uri: Optional[str] = None (alias 'trainingDatasetUri')
    -

    Required. Cloud Storage path to file containing training dataset for preference optimization tuning. The dataset must be formatted as a JSONL file.

    -
    - -
    -
    -field validation_dataset_uri: Optional[str] = None (alias 'validationDatasetUri')
    -

    Optional. Cloud Storage path to file containing validation dataset for preference optimization tuning. The dataset must be formatted as a JSONL file.

    -
    - -
    - -
    -
    -class genai.types.PreferenceOptimizationSpecDict
    -

    Bases: TypedDict

    -

    Tuning Spec for Preference Optimization.

    -
    -
    -hyper_parameters: Optional[PreferenceOptimizationHyperParametersDict]
    -

    Optional. Hyperparameters for Preference Optimization.

    -
    - -
    -
    -training_dataset_uri: Optional[str]
    -

    Required. Cloud Storage path to file containing training dataset for preference optimization tuning. The dataset must be formatted as a JSONL file.

    -
    - -
    -
    -validation_dataset_uri: Optional[str]
    -

    Optional. Cloud Storage path to file containing validation dataset for preference optimization tuning. The dataset must be formatted as a JSONL file.

    -
    - -
    -
    pydantic model genai.types.ProactivityConfig
    @@ -127235,6 +126406,119 @@

    Submodules +
    +pydantic model genai.types.RougeSpec
    +

    Bases: BaseModel

    +

    Spec for rouge metric.

    +

    Create a new model by parsing and validating input data from keyword arguments.

    +

    Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be +validated to form a valid model.

    +

    self is explicitly positional-only to allow self as a field name.

    +

    +Show JSON schema
    {
+   "title": "RougeSpec",
+   "description": "Spec for rouge metric.",
+   "type": "object",
+   "properties": {
+      "rougeType": {
+         "anyOf": [
+            {
+               "type": "string"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "Optional. Supported rouge types are rougen[1-9], rougeL, and rougeLsum.",
+         "title": "Rougetype"
+      },
+      "splitSummaries": {
+         "anyOf": [
+            {
+               "type": "boolean"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "Optional. Whether to split summaries while using rougeLsum.",
+         "title": "Splitsummaries"
+      },
+      "useStemmer": {
+         "anyOf": [
+            {
+               "type": "boolean"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "Optional. Whether to use stemmer to compute rouge score.",
+         "title": "Usestemmer"
+      }
+   },
+   "additionalProperties": false
+}
+
    +
    +

    +
    Fields:
    +
    +
    +
    +
    +
    +field rouge_type: Optional[str] = None (alias 'rougeType')
    +

    Optional. Supported rouge types are rougen[1-9], rougeL, and rougeLsum.

    +
    + +
    +
    +field split_summaries: Optional[bool] = None (alias 'splitSummaries')
    +

    Optional. Whether to split summaries while using rougeLsum.

    +
    + +
    +
    +field use_stemmer: Optional[bool] = None (alias 'useStemmer')
    +

    Optional. Whether to use stemmer to compute rouge score.

    +
    + +

    + +
    +
    +class genai.types.RougeSpecDict
    +

    Bases: TypedDict

    +

    Spec for rouge metric.

    +
    +
    +rouge_type: Optional[str]
    +

    Optional. Supported rouge types are rougen[1-9], rougeL, and rougeLsum.

    +
    + +
    +
    +split_summaries: Optional[bool]
    +

    Optional. Whether to split summaries while using rougeLsum.

    +
    + +
    +
    +use_stemmer: Optional[bool]
    +

    Optional. Whether to use stemmer to compute rouge score.

    +
    + +
    +
    pydantic model genai.types.SafetyAttributes
    @@ -128671,6 +127955,116 @@

    Submodules +
    +pydantic model genai.types.ScribbleImage
    +

    Bases: BaseModel

    +

    An image mask representing a brush scribble.

    +

    Create a new model by parsing and validating input data from keyword arguments.

    +

    Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be +validated to form a valid model.

    +

    self is explicitly positional-only to allow self as a field name.

    +

    +Show JSON schema
    {
+   "title": "ScribbleImage",
+   "description": "An image mask representing a brush scribble.",
+   "type": "object",
+   "properties": {
+      "image": {
+         "anyOf": [
+            {
+               "$ref": "#/$defs/Image"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "The brush scribble to guide segmentation. Valid for the interactive mode."
+      }
+   },
+   "$defs": {
+      "Image": {
+         "additionalProperties": false,
+         "description": "An image.",
+         "properties": {
+            "gcsUri": {
+               "anyOf": [
+                  {
+                     "type": "string"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "The Cloud Storage URI of the image. ``Image`` can contain a value\n      for this field or the ``image_bytes`` field but not both.\n      ",
+               "title": "Gcsuri"
+            },
+            "imageBytes": {
+               "anyOf": [
+                  {
+                     "format": "base64url",
+                     "type": "string"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "The image bytes data. ``Image`` can contain a value for this field\n      or the ``gcs_uri`` field but not both.\n      ",
+               "title": "Imagebytes"
+            },
+            "mimeType": {
+               "anyOf": [
+                  {
+                     "type": "string"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "The MIME type of the image.",
+               "title": "Mimetype"
+            }
+         },
+         "title": "Image",
+         "type": "object"
+      }
+   },
+   "additionalProperties": false
+}
+
    +
    +

    +
    Fields:
    +
    +
    +
    +
    +
    +field image: Optional[Image] = None
    +

    The brush scribble to guide segmentation. Valid for the interactive mode.

    +
    + +

    + +
    +
    +class genai.types.ScribbleImageDict
    +

    Bases: TypedDict

    +

    An image mask representing a brush scribble.

    +
    +
    +image: Optional[ImageDict]
    +

    The brush scribble to guide segmentation. Valid for the interactive mode.

    +
    + +
    +
    pydantic model genai.types.SearchEntryPoint
    @@ -128898,6 +128292,827 @@

    Submodules +
    +pydantic model genai.types.SegmentImageConfig
    +

    Bases: BaseModel

    +

    Configuration for segmenting an image.

    +

    Create a new model by parsing and validating input data from keyword arguments.

    +

    Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be +validated to form a valid model.

    +

    self is explicitly positional-only to allow self as a field name.

    +

    +Show JSON schema
    {
+   "title": "SegmentImageConfig",
+   "description": "Configuration for segmenting an image.",
+   "type": "object",
+   "properties": {
+      "httpOptions": {
+         "anyOf": [
+            {
+               "$ref": "#/$defs/HttpOptions"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "Used to override HTTP request options."
+      },
+      "mode": {
+         "anyOf": [
+            {
+               "$ref": "#/$defs/SegmentMode"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "The segmentation mode to use."
+      },
+      "maxPredictions": {
+         "anyOf": [
+            {
+               "type": "integer"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "The maximum number of predictions to return up to, by top\n      confidence score.",
+         "title": "Maxpredictions"
+      },
+      "confidenceThreshold": {
+         "anyOf": [
+            {
+               "type": "number"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "The confidence score threshold for the detections as a decimal\n      value. Only predictions with a confidence score higher than this\n      threshold will be returned.",
+         "title": "Confidencethreshold"
+      },
+      "maskDilation": {
+         "anyOf": [
+            {
+               "type": "number"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "A decimal value representing how much dilation to apply to the\n      masks. 0 for no dilation. 1.0 means the masked area covers the whole\n      image.",
+         "title": "Maskdilation"
+      },
+      "binaryColorThreshold": {
+         "anyOf": [
+            {
+               "type": "number"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "The binary color threshold to apply to the masks. The threshold\n      can be set to a decimal value between 0 and 255 non-inclusive.\n      Set to -1 for no binary color thresholding.",
+         "title": "Binarycolorthreshold"
+      }
+   },
+   "$defs": {
+      "HttpOptions": {
+         "additionalProperties": false,
+         "description": "HTTP options to be used in each of the requests.",
+         "properties": {
+            "baseUrl": {
+               "anyOf": [
+                  {
+                     "type": "string"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "The base URL for the AI platform service endpoint.",
+               "title": "Baseurl"
+            },
+            "apiVersion": {
+               "anyOf": [
+                  {
+                     "type": "string"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Specifies the version of the API to use.",
+               "title": "Apiversion"
+            },
+            "headers": {
+               "anyOf": [
+                  {
+                     "additionalProperties": {
+                        "type": "string"
+                     },
+                     "type": "object"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Additional HTTP headers to be sent with the request.",
+               "title": "Headers"
+            },
+            "timeout": {
+               "anyOf": [
+                  {
+                     "type": "integer"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Timeout for the request in milliseconds.",
+               "title": "Timeout"
+            },
+            "clientArgs": {
+               "anyOf": [
+                  {
+                     "type": "object"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Args passed to the HTTP client.",
+               "title": "Clientargs"
+            },
+            "asyncClientArgs": {
+               "anyOf": [
+                  {
+                     "type": "object"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Args passed to the async HTTP client.",
+               "title": "Asyncclientargs"
+            },
+            "extraBody": {
+               "anyOf": [
+                  {
+                     "type": "object"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Extra parameters to add to the request body.\n      The structure must match the backend API's request structure.\n      - VertexAI backend API docs: https://cloud.google.com/vertex-ai/docs/reference/rest\n      - GeminiAPI backend API docs: https://ai.google.dev/api/rest",
+               "title": "Extrabody"
+            },
+            "retryOptions": {
+               "anyOf": [
+                  {
+                     "$ref": "#/$defs/HttpRetryOptions"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "HTTP retry options for the request."
+            }
+         },
+         "title": "HttpOptions",
+         "type": "object"
+      },
+      "HttpRetryOptions": {
+         "additionalProperties": false,
+         "description": "HTTP retry options to be used in each of the requests.",
+         "properties": {
+            "attempts": {
+               "anyOf": [
+                  {
+                     "type": "integer"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Maximum number of attempts, including the original request.\n      If 0 or 1, it means no retries.",
+               "title": "Attempts"
+            },
+            "initialDelay": {
+               "anyOf": [
+                  {
+                     "type": "number"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Initial delay before the first retry, in fractions of a second.",
+               "title": "Initialdelay"
+            },
+            "maxDelay": {
+               "anyOf": [
+                  {
+                     "type": "number"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Maximum delay between retries, in fractions of a second.",
+               "title": "Maxdelay"
+            },
+            "expBase": {
+               "anyOf": [
+                  {
+                     "type": "number"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Multiplier by which the delay increases after each attempt.",
+               "title": "Expbase"
+            },
+            "jitter": {
+               "anyOf": [
+                  {
+                     "type": "number"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "Randomness factor for the delay.",
+               "title": "Jitter"
+            },
+            "httpStatusCodes": {
+               "anyOf": [
+                  {
+                     "items": {
+                        "type": "integer"
+                     },
+                     "type": "array"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "List of HTTP status codes that should trigger a retry.\n      If not specified, a default set of retryable codes may be used.",
+               "title": "Httpstatuscodes"
+            }
+         },
+         "title": "HttpRetryOptions",
+         "type": "object"
+      },
+      "SegmentMode": {
+         "description": "Enum that represents the segmentation mode.",
+         "enum": [
+            "FOREGROUND",
+            "BACKGROUND",
+            "PROMPT",
+            "SEMANTIC",
+            "INTERACTIVE"
+         ],
+         "title": "SegmentMode",
+         "type": "string"
+      }
+   },
+   "additionalProperties": false
+}
+
    +
    +

    +
    Fields:
    +
    +
    +
    +
    +
    +field binary_color_threshold: Optional[float] = None (alias 'binaryColorThreshold')
    +

    The binary color threshold to apply to the masks. The threshold +can be set to a decimal value between 0 and 255 non-inclusive. +Set to -1 for no binary color thresholding.

    +
    + +
    +
    +field confidence_threshold: Optional[float] = None (alias 'confidenceThreshold')
    +

    The confidence score threshold for the detections as a decimal +value. Only predictions with a confidence score higher than this +threshold will be returned.

    +
    + +
    +
    +field http_options: Optional[HttpOptions] = None (alias 'httpOptions')
    +

    Used to override HTTP request options.

    +
    + +
    +
    +field mask_dilation: Optional[float] = None (alias 'maskDilation')
    +

    A decimal value representing how much dilation to apply to the +masks. 0 for no dilation. 1.0 means the masked area covers the whole +image.

    +
    + +
    +
    +field max_predictions: Optional[int] = None (alias 'maxPredictions')
    +

    The maximum number of predictions to return up to, by top +confidence score.

    +
    + +
    +
    +field mode: Optional[SegmentMode] = None
    +

    The segmentation mode to use.

    +
    + +

    + +
    +
    +class genai.types.SegmentImageConfigDict
    +

    Bases: TypedDict

    +

    Configuration for segmenting an image.

    +
    +
    +binary_color_threshold: Optional[float]
    +

    The binary color threshold to apply to the masks. The threshold +can be set to a decimal value between 0 and 255 non-inclusive. +Set to -1 for no binary color thresholding.

    +
    + +
    +
    +confidence_threshold: Optional[float]
    +

    The confidence score threshold for the detections as a decimal +value. Only predictions with a confidence score higher than this +threshold will be returned.

    +
    + +
    +
    +http_options: Optional[HttpOptionsDict]
    +

    Used to override HTTP request options.

    +
    + +
    +
    +mask_dilation: Optional[float]
    +

    A decimal value representing how much dilation to apply to the +masks. 0 for no dilation. 1.0 means the masked area covers the whole +image.

    +
    + +
    +
    +max_predictions: Optional[int]
    +

    The maximum number of predictions to return up to, by top +confidence score.

    +
    + +
    +
    +mode: Optional[SegmentMode]
    +

    The segmentation mode to use.

    +
    + +
    + +
    +
    +pydantic model genai.types.SegmentImageResponse
    +

    Bases: BaseModel

    +

    The output images response.

    +

    Create a new model by parsing and validating input data from keyword arguments.

    +

    Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be +validated to form a valid model.

    +

    self is explicitly positional-only to allow self as a field name.

    +

    +Show JSON schema
    {
+   "title": "SegmentImageResponse",
+   "description": "The output images response.",
+   "type": "object",
+   "properties": {
+      "generatedMasks": {
+         "anyOf": [
+            {
+               "items": {
+                  "$ref": "#/$defs/GeneratedImageMask"
+               },
+               "type": "array"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "List of generated image masks.\n      ",
+         "title": "Generatedmasks"
+      }
+   },
+   "$defs": {
+      "EntityLabel": {
+         "additionalProperties": false,
+         "description": "An entity representing the segmented area.",
+         "properties": {
+            "label": {
+               "anyOf": [
+                  {
+                     "type": "string"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "The label of the segmented entity.",
+               "title": "Label"
+            },
+            "score": {
+               "anyOf": [
+                  {
+                     "type": "number"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "The confidence score of the detected label.",
+               "title": "Score"
+            }
+         },
+         "title": "EntityLabel",
+         "type": "object"
+      },
+      "GeneratedImageMask": {
+         "additionalProperties": false,
+         "description": "A generated image mask.",
+         "properties": {
+            "mask": {
+               "anyOf": [
+                  {
+                     "$ref": "#/$defs/Image"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "The generated image mask."
+            },
+            "labels": {
+               "anyOf": [
+                  {
+                     "items": {
+                        "$ref": "#/$defs/EntityLabel"
+                     },
+                     "type": "array"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "The detected entities on the segmented area.",
+               "title": "Labels"
+            }
+         },
+         "title": "GeneratedImageMask",
+         "type": "object"
+      },
+      "Image": {
+         "additionalProperties": false,
+         "description": "An image.",
+         "properties": {
+            "gcsUri": {
+               "anyOf": [
+                  {
+                     "type": "string"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "The Cloud Storage URI of the image. ``Image`` can contain a value\n      for this field or the ``image_bytes`` field but not both.\n      ",
+               "title": "Gcsuri"
+            },
+            "imageBytes": {
+               "anyOf": [
+                  {
+                     "format": "base64url",
+                     "type": "string"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "The image bytes data. ``Image`` can contain a value for this field\n      or the ``gcs_uri`` field but not both.\n      ",
+               "title": "Imagebytes"
+            },
+            "mimeType": {
+               "anyOf": [
+                  {
+                     "type": "string"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "The MIME type of the image.",
+               "title": "Mimetype"
+            }
+         },
+         "title": "Image",
+         "type": "object"
+      }
+   },
+   "additionalProperties": false
+}
+
    +
    +

    +
    Fields:
    +
    +
    +
    +
    +
    +field generated_masks: Optional[list[GeneratedImageMask]] = None (alias 'generatedMasks')
    +

    List of generated image masks.

    +
    + +
    + +
    +
    +class genai.types.SegmentImageResponseDict
    +

    Bases: TypedDict

    +

    The output images response.

    +
    +
    +generated_masks: Optional[list[GeneratedImageMaskDict]]
    +

    List of generated image masks.

    +
    + +
    + +
    +
    +pydantic model genai.types.SegmentImageSource
    +

    Bases: BaseModel

    +

    A set of source input(s) for image segmentation.

    +

    Create a new model by parsing and validating input data from keyword arguments.

    +

    Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be +validated to form a valid model.

    +

    self is explicitly positional-only to allow self as a field name.

    +

    +Show JSON schema
    {
+   "title": "SegmentImageSource",
+   "description": "A set of source input(s) for image segmentation.",
+   "type": "object",
+   "properties": {
+      "prompt": {
+         "anyOf": [
+            {
+               "type": "string"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "A text prompt for guiding the model during image segmentation.\n      Required for prompt mode and semantic mode, disallowed for other modes.",
+         "title": "Prompt"
+      },
+      "image": {
+         "anyOf": [
+            {
+               "$ref": "#/$defs/Image"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "The image to be segmented."
+      },
+      "scribbleImage": {
+         "anyOf": [
+            {
+               "$ref": "#/$defs/ScribbleImage"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "The brush scribble to guide segmentation.\n      Required for the interactive mode, disallowed for other modes."
+      }
+   },
+   "$defs": {
+      "Image": {
+         "additionalProperties": false,
+         "description": "An image.",
+         "properties": {
+            "gcsUri": {
+               "anyOf": [
+                  {
+                     "type": "string"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "The Cloud Storage URI of the image. ``Image`` can contain a value\n      for this field or the ``image_bytes`` field but not both.\n      ",
+               "title": "Gcsuri"
+            },
+            "imageBytes": {
+               "anyOf": [
+                  {
+                     "format": "base64url",
+                     "type": "string"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "The image bytes data. ``Image`` can contain a value for this field\n      or the ``gcs_uri`` field but not both.\n      ",
+               "title": "Imagebytes"
+            },
+            "mimeType": {
+               "anyOf": [
+                  {
+                     "type": "string"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "The MIME type of the image.",
+               "title": "Mimetype"
+            }
+         },
+         "title": "Image",
+         "type": "object"
+      },
+      "ScribbleImage": {
+         "additionalProperties": false,
+         "description": "An image mask representing a brush scribble.",
+         "properties": {
+            "image": {
+               "anyOf": [
+                  {
+                     "$ref": "#/$defs/Image"
+                  },
+                  {
+                     "type": "null"
+                  }
+               ],
+               "default": null,
+               "description": "The brush scribble to guide segmentation. Valid for the interactive mode."
+            }
+         },
+         "title": "ScribbleImage",
+         "type": "object"
+      }
+   },
+   "additionalProperties": false
+}
+
    +
    +

    +
    Fields:
    +
    +
    +
    +
    +
    +field image: Optional[Image] = None
    +

    The image to be segmented.

    +
    + +
    +
    +field prompt: Optional[str] = None
    +

    A text prompt for guiding the model during image segmentation. +Required for prompt mode and semantic mode, disallowed for other modes.

    +
    + +
    +
    +field scribble_image: Optional[ScribbleImage] = None (alias 'scribbleImage')
    +

    The brush scribble to guide segmentation. +Required for the interactive mode, disallowed for other modes.

    +
    + +
    + +
    +
    +class genai.types.SegmentImageSourceDict
    +

    Bases: TypedDict

    +

    A set of source input(s) for image segmentation.

    +
    +
    +image: Optional[ImageDict]
    +

    The image to be segmented.

    +
    + +
    +
    +prompt: Optional[str]
    +

    A text prompt for guiding the model during image segmentation. +Required for prompt mode and semantic mode, disallowed for other modes.

    +
    + +
    +
    +scribble_image: Optional[ScribbleImageDict]
    +

    The brush scribble to guide segmentation. +Required for the interactive mode, disallowed for other modes.

    +
    + +
    + +
    +
    +class genai.types.SegmentMode(*values)
    +

    Bases: CaseInSensitiveEnum

    +

    Enum that represents the segmentation mode.

    +
    +
    +BACKGROUND = 'BACKGROUND'
    +
    + +
    +
    +FOREGROUND = 'FOREGROUND'
    +
    + +
    +
    +INTERACTIVE = 'INTERACTIVE'
    +
    + +
    +
    +PROMPT = 'PROMPT'
    +
    + +
    +
    +SEMANTIC = 'SEMANTIC'
    +
    + +
    +
    pydantic model genai.types.SessionResumptionConfig
    @@ -132931,6 +133146,18 @@

    Submodules "default": null, "description": "Optional. Tool to support URL context retrieval." }, + "computerUse": { + "anyOf": [ + { + "$ref": "#/$defs/ToolComputerUse" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. Tool to support the model interacting directly with the\n computer. If enabled, it automatically populates computer-use specific\n Function Declarations." + }, "codeExecution": { "anyOf": [ { @@ -133289,10 +133516,36 @@

    Submodules "EnterpriseWebSearch": { "additionalProperties": false, "description": "Tool to search public web data, powered by Vertex AI Search and Sec4 compliance.", - "properties": {}, + "properties": { + "excludeDomains": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. List of domains to be excluded from the search results. The default limit is 2000 domains.", + "title": "Excludedomains" + } + }, "title": "EnterpriseWebSearch", "type": "object" }, + "Environment": { + "description": "The environment being operated.", + "enum": [ + "ENVIRONMENT_UNSPECIFIED", + "ENVIRONMENT_BROWSER" + ], + "title": "Environment", + "type": "string" + }, "ExternalApi": { "additionalProperties": false, "description": "Retrieve from data source powered by external API for grounding.\n\nThe external API is not owned by Google, but need to follow the pre-defined\nAPI spec.", @@ -133555,6 +133808,22 @@

    Submodules ], "default": null, "description": "Optional. Filter search results to a specific time range.\n If customers set a start time, they must set an end time (and vice versa).\n " + }, + "excludeDomains": { + "anyOf": [ + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Optional. List of domains to be excluded from the search results.\n The default limit is 2000 domains.", + "title": "Excludedomains" } }, "title": "GoogleSearch", @@ -134222,6 +134491,26 @@

    Submodules "title": "ToolCodeExecution", "type": "object" }, + "ToolComputerUse": { + "additionalProperties": false, + "description": "Tool to support computer use.", + "properties": { + "environment": { + "anyOf": [ + { + "$ref": "#/$defs/Environment" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Required. The environment being operated." + } + }, + "title": "ToolComputerUse", + "type": "object" + }, "Type": { "description": "Optional. The type of the data.", "enum": [ @@ -134491,6 +134780,7 @@

    SubmodulesFields:

    +
    @@ -134586,6 +134884,77 @@

    Submodules +
    +pydantic model genai.types.ToolComputerUse
    +

    Bases: BaseModel

    +

    Tool to support computer use.

    +

    Create a new model by parsing and validating input data from keyword arguments.

    +

    Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be +validated to form a valid model.

    +

    self is explicitly positional-only to allow self as a field name.

    +

    +Show JSON schema
    {
+   "title": "ToolComputerUse",
+   "description": "Tool to support computer use.",
+   "type": "object",
+   "properties": {
+      "environment": {
+         "anyOf": [
+            {
+               "$ref": "#/$defs/Environment"
+            },
+            {
+               "type": "null"
+            }
+         ],
+         "default": null,
+         "description": "Required. The environment being operated."
+      }
+   },
+   "$defs": {
+      "Environment": {
+         "description": "The environment being operated.",
+         "enum": [
+            "ENVIRONMENT_UNSPECIFIED",
+            "ENVIRONMENT_BROWSER"
+         ],
+         "title": "Environment",
+         "type": "string"
+      }
+   },
+   "additionalProperties": false
+}
+
    +
    +

    +
    Fields:
    +
    +
    +
    +
    +
    +field environment: Optional[Environment] = None
    +

    Required. The environment being operated.

    +
    + +

    + +
    +
    +class genai.types.ToolComputerUseDict
    +

    Bases: TypedDict

    +

    Tool to support computer use.

    +
    +
    +environment: Optional[Environment]
    +

    Required. The environment being operated.

    +
    + +
    +
    pydantic model genai.types.ToolConfig
    @@ -134800,6 +135169,14 @@

    Submodules +
    +computer_use: Optional[ToolComputerUseDict]
    +

    Optional. Tool to support the model interacting directly with the +computer. If enabled, it automatically populates computer-use specific +Function Declarations.

    +

    +
    @@ -137120,7 +137497,6 @@

    Submodules Show JSON schema
    {
    "title": "TuningJob",
-   "description": "A tuning job.",
    "type": "object",
    "properties": {
       "sdkHttpResponse": {
@@ -137326,6 +137702,10 @@ 
    Submodules         "default": null,
          "description": "Tuning Spec for open sourced and third party Partner models."
       },
+      "evaluationConfig": {
+         "default": null,
+         "title": "Evaluationconfig"
+      },
       "customBaseModel": {
          "anyOf": [
             {
@@ -137339,18 +137719,6 @@ 
    Submodules         "description": "Optional. The user-provided path to custom model weights. Set this field to tune a custom model. The path must be a Cloud Storage directory that contains the model weights in .safetensors format along with associated model metadata files. If this field is set, the base_model field must still be set to indicate which base model the custom model is derived from. This feature is only available for open source models.",
          "title": "Custombasemodel"
       },
-      "distillationSpec": {
-         "anyOf": [
-            {
-               "$ref": "#/$defs/DistillationSpec"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "Tuning Spec for Distillation."
-      },
       "experiment": {
          "anyOf": [
             {
@@ -137406,44 +137774,6 @@ 
    Submodules         "description": "Output only. The resource name of the PipelineJob associated with the TuningJob. Format: `projects/{project}/locations/{location}/pipelineJobs/{pipeline_job}`.",
          "title": "Pipelinejob"
       },
-      "preferenceOptimizationSpec": {
-         "anyOf": [
-            {
-               "$ref": "#/$defs/PreferenceOptimizationSpec"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "Tuning Spec for Preference Optimization."
-      },
-      "satisfiesPzi": {
-         "anyOf": [
-            {
-               "type": "boolean"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "Output only. Reserved for future use.",
-         "title": "Satisfiespzi"
-      },
-      "satisfiesPzs": {
-         "anyOf": [
-            {
-               "type": "boolean"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "Output only. Reserved for future use.",
-         "title": "Satisfiespzs"
-      },
       "serviceAccount": {
          "anyOf": [
             {
@@ -137469,18 +137799,6 @@ 
    Submodules         "default": null,
          "description": "Optional. The display name of the TunedModel. The name can be up to 128 characters long and can consist of any UTF-8 characters.",
          "title": "Tunedmodeldisplayname"
-      },
-      "veoTuningSpec": {
-         "anyOf": [
-            {
-               "$ref": "#/$defs/VeoTuningSpec"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "Tuning Spec for Veo Tuning."
       }
    },
    "$defs": {
@@ -137910,150 +138228,6 @@ 
    Submodules         "title": "DistillationDataStats",
          "type": "object"
       },
-      "DistillationHyperParameters": {
-         "additionalProperties": false,
-         "description": "Hyperparameters for Distillation.",
-         "properties": {
-            "adapterSize": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/AdapterSize"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Adapter size for distillation."
-            },
-            "epochCount": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Number of complete passes the model makes over the entire training dataset during training.",
-               "title": "Epochcount"
-            },
-            "learningRateMultiplier": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Multiplier for adjusting the default learning rate.",
-               "title": "Learningratemultiplier"
-            }
-         },
-         "title": "DistillationHyperParameters",
-         "type": "object"
-      },
-      "DistillationSpec": {
-         "additionalProperties": false,
-         "description": "Tuning Spec for Distillation.",
-         "properties": {
-            "baseTeacherModel": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The base teacher model that is being distilled. See [Supported models](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/tuning#supported_models).",
-               "title": "Baseteachermodel"
-            },
-            "hyperParameters": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/DistillationHyperParameters"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Hyperparameters for Distillation."
-            },
-            "pipelineRootDirectory": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Deprecated. A path in a Cloud Storage bucket, which will be treated as the root output directory of the distillation pipeline. It is used by the system to generate the paths of output artifacts.",
-               "title": "Pipelinerootdirectory"
-            },
-            "studentModel": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The student model that is being tuned, e.g., \"google/gemma-2b-1.1-it\". Deprecated. Use base_model instead.",
-               "title": "Studentmodel"
-            },
-            "trainingDatasetUri": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Deprecated. Cloud Storage path to file containing training dataset for tuning. The dataset must be formatted as a JSONL file.",
-               "title": "Trainingdataseturi"
-            },
-            "tunedTeacherModelSource": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "The resource name of the Tuned teacher model. Format: `projects/{project}/locations/{location}/models/{model}`.",
-               "title": "Tunedteachermodelsource"
-            },
-            "validationDatasetUri": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Cloud Storage path to file containing validation dataset for tuning. The dataset must be formatted as a JSONL file.",
-               "title": "Validationdataseturi"
-            }
-         },
-         "title": "DistillationSpec",
-         "type": "object"
-      },
       "EncryptionSpec": {
          "additionalProperties": false,
          "description": "Represents a customer-managed encryption key spec that can be applied to a top-level resource.",
@@ -138821,111 +138995,6 @@ 
    Submodules         "title": "PreferenceOptimizationDataStats",
          "type": "object"
       },
-      "PreferenceOptimizationHyperParameters": {
-         "additionalProperties": false,
-         "description": "Hyperparameters for Preference Optimization.",
-         "properties": {
-            "adapterSize": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/AdapterSize"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Adapter size for preference optimization."
-            },
-            "beta": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Weight for KL Divergence regularization.",
-               "title": "Beta"
-            },
-            "epochCount": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Number of complete passes the model makes over the entire training dataset during training.",
-               "title": "Epochcount"
-            },
-            "learningRateMultiplier": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Multiplier for adjusting the default learning rate.",
-               "title": "Learningratemultiplier"
-            }
-         },
-         "title": "PreferenceOptimizationHyperParameters",
-         "type": "object"
-      },
-      "PreferenceOptimizationSpec": {
-         "additionalProperties": false,
-         "description": "Tuning Spec for Preference Optimization.",
-         "properties": {
-            "hyperParameters": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/PreferenceOptimizationHyperParameters"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Hyperparameters for Preference Optimization."
-            },
-            "trainingDatasetUri": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Required. Cloud Storage path to file containing training dataset for preference optimization tuning. The dataset must be formatted as a JSONL file.",
-               "title": "Trainingdataseturi"
-            },
-            "validationDatasetUri": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Cloud Storage path to file containing validation dataset for preference optimization tuning. The dataset must be formatted as a JSONL file.",
-               "title": "Validationdataseturi"
-            }
-         },
-         "title": "PreferenceOptimizationSpec",
-         "type": "object"
-      },
       "SupervisedHyperParameters": {
          "additionalProperties": false,
          "description": "Hyperparameters for SFT.",
@@ -139577,108 +139646,6 @@ 
    Submodules         "title": "TuningMode",
          "type": "string"
       },
-      "TuningTask": {
-         "description": "Optional. The tuning task. Either I2V or T2V.",
-         "enum": [
-            "TUNING_TASK_UNSPECIFIED",
-            "TUNING_TASK_I2V",
-            "TUNING_TASK_T2V"
-         ],
-         "title": "TuningTask",
-         "type": "string"
-      },
-      "VeoHyperParameters": {
-         "additionalProperties": false,
-         "description": "Hyperparameters for Veo.",
-         "properties": {
-            "epochCount": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Number of complete passes the model makes over the entire training dataset during training.",
-               "title": "Epochcount"
-            },
-            "learningRateMultiplier": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Multiplier for adjusting the default learning rate.",
-               "title": "Learningratemultiplier"
-            },
-            "tuningTask": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/TuningTask"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. The tuning task. Either I2V or T2V."
-            }
-         },
-         "title": "VeoHyperParameters",
-         "type": "object"
-      },
-      "VeoTuningSpec": {
-         "additionalProperties": false,
-         "description": "Tuning Spec for Veo Model Tuning.",
-         "properties": {
-            "hyperParameters": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/VeoHyperParameters"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Hyperparameters for Veo."
-            },
-            "trainingDatasetUri": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Required. Training dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.",
-               "title": "Trainingdataseturi"
-            },
-            "validationDatasetUri": {
-               "anyOf": [
-                  {
-                     "type": "string"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Validation dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.",
-               "title": "Validationdataseturi"
-            }
-         },
-         "title": "VeoTuningSpec",
-         "type": "object"
-      },
       "VideoMetadata": {
          "additionalProperties": false,
          "description": "Describes how the video in the Part should be used by the model.",
@@ -139738,10 +139705,10 @@ 
    Submodulescreate_time (datetime.datetime | None)
    
 
  • custom_base_model (str | None)
    • 
 
  • description (str | None)
    • 
-
  • distillation_spec (genai.types.DistillationSpec | None)
    • 
 
  • encryption_spec (genai.types.EncryptionSpec | None)
    • 
 
  • end_time (datetime.datetime | None)
    • 
 
  • error (genai.types.GoogleRpcStatus | None)
    • 
+
  • evaluation_config (genai.types.EvaluationConfig | None)
    • 
 
  • experiment (str | None)
    • 
 
  • labels (dict[str, str] | None)
    • 
 
  • name (str | None)
    • 
@@ -139749,9 +139716,6 @@ 
    Submodulespartner_model_tuning_spec (genai.types.PartnerModelTuningSpec | None)
    
 
  • pipeline_job (str | None)
    • 
 
  • pre_tuned_model (genai.types.PreTunedModel | None)
    • 
-
  • preference_optimization_spec (genai.types.PreferenceOptimizationSpec | None)
    • 
-
  • satisfies_pzi (bool | None)
    • 
-
  • satisfies_pzs (bool | None)
    • 
 
  • sdk_http_response (genai.types.HttpResponse | None)
    • 
 
  • service_account (str | None)
    • 
 
  • start_time (datetime.datetime | None)
    • 
@@ -139761,7 +139725,6 @@ 
    Submodulestuned_model_display_name (str | None)
    
 
  • tuning_data_stats (genai.types.TuningDataStats | None)
    • 
 
  • update_time (datetime.datetime | None)
    • 
-
  • veo_tuning_spec (genai.types.VeoTuningSpec | None)
    •

    @@ -139789,12 +139752,6 @@

    Submodules -
    -field distillation_spec: Optional[DistillationSpec] = None (alias 'distillationSpec')
    -

    Tuning Spec for Distillation.

    -

    -
    field encryption_spec: Optional[EncryptionSpec] = None (alias 'encryptionSpec')
    @@ -139813,6 +139770,11 @@

    Submodules +
    +field evaluation_config: Optional[EvaluationConfig] = None (alias 'evaluationConfig')
    +

    +
    field experiment: Optional[str] = None
    @@ -139855,24 +139817,6 @@

    Submodules -
    -field preference_optimization_spec: Optional[PreferenceOptimizationSpec] = None (alias 'preferenceOptimizationSpec')
    -

    Tuning Spec for Preference Optimization.

    -

    - -
    -
    -field satisfies_pzi: Optional[bool] = None (alias 'satisfiesPzi')
    -

    Output only. Reserved for future use.

    -
    - -
    -
    -field satisfies_pzs: Optional[bool] = None (alias 'satisfiesPzs')
    -

    Output only. Reserved for future use.

    -
    -
    field sdk_http_response: Optional[HttpResponse] = None (alias 'sdkHttpResponse')
    @@ -139927,12 +139871,6 @@

    Submodules -
    -field veo_tuning_spec: Optional[VeoTuningSpec] = None (alias 'veoTuningSpec')
    -

    Tuning Spec for Veo Tuning.

    -

    -
    property has_ended: bool
    @@ -139981,12 +139919,6 @@

    Submodules -
    -distillation_spec: Optional[DistillationSpecDict]
    -

    Tuning Spec for Distillation.

    -

    -
    encryption_spec: Optional[EncryptionSpecDict]
    @@ -140010,6 +139942,11 @@

    Submodules +
    +evaluation_config: Optional[EvaluationConfigDict]
    +

    +
    experiment: Optional[str]
    @@ -140067,24 +140004,6 @@

    Submodules -
    -preference_optimization_spec: Optional[PreferenceOptimizationSpecDict]
    -

    Tuning Spec for Preference Optimization.

    -

    - -
    -
    -satisfies_pzi: Optional[bool]
    -

    Output only. Reserved for future use.

    -
    - -
    -
    -satisfies_pzs: Optional[bool]
    -

    Output only. Reserved for future use.

    -
    -
    sdk_http_response: Optional[HttpResponseDict]
    @@ -140144,12 +140063,6 @@

    Submodules -
    -veo_tuning_spec: Optional[VeoTuningSpecDict]
    -

    Tuning Spec for Veo Tuning.

    -

    -

    @@ -140380,31 +140293,6 @@

    Submodules -
    -class genai.types.TuningTask(*values)
    -

    Bases: CaseInSensitiveEnum

    -

    Optional. The tuning task. Either I2V or T2V.

    -
    -
    -TUNING_TASK_I2V = 'TUNING_TASK_I2V'
    -

    Tuning task for image to video.

    -
    - -
    -
    -TUNING_TASK_T2V = 'TUNING_TASK_T2V'
    -

    Tuning task for text to video.

    -
    - -
    -
    -TUNING_TASK_UNSPECIFIED = 'TUNING_TASK_UNSPECIFIED'
    -

    Default value. This value is unused.

    -
    - -

    -
    pydantic model genai.types.TuningValidationDataset
    @@ -143999,300 +143887,6 @@

    Submodules -
    -pydantic model genai.types.VeoHyperParameters
    -

    Bases: BaseModel

    -

    Hyperparameters for Veo.

    -

    Create a new model by parsing and validating input data from keyword arguments.

    -

    Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be -validated to form a valid model.

    -

    self is explicitly positional-only to allow self as a field name.

    -

    -Show JSON schema
    {
-   "title": "VeoHyperParameters",
-   "description": "Hyperparameters for Veo.",
-   "type": "object",
-   "properties": {
-      "epochCount": {
-         "anyOf": [
-            {
-               "type": "integer"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "Optional. Number of complete passes the model makes over the entire training dataset during training.",
-         "title": "Epochcount"
-      },
-      "learningRateMultiplier": {
-         "anyOf": [
-            {
-               "type": "number"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "Optional. Multiplier for adjusting the default learning rate.",
-         "title": "Learningratemultiplier"
-      },
-      "tuningTask": {
-         "anyOf": [
-            {
-               "$ref": "#/$defs/TuningTask"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "Optional. The tuning task. Either I2V or T2V."
-      }
-   },
-   "$defs": {
-      "TuningTask": {
-         "description": "Optional. The tuning task. Either I2V or T2V.",
-         "enum": [
-            "TUNING_TASK_UNSPECIFIED",
-            "TUNING_TASK_I2V",
-            "TUNING_TASK_T2V"
-         ],
-         "title": "TuningTask",
-         "type": "string"
-      }
-   },
-   "additionalProperties": false
-}
-
    -
    -

    -
    Fields:
    -
    -
    -
    -
    -
    -field epoch_count: Optional[int] = None (alias 'epochCount')
    -

    Optional. Number of complete passes the model makes over the entire training dataset during training.

    -
    - -
    -
    -field learning_rate_multiplier: Optional[float] = None (alias 'learningRateMultiplier')
    -

    Optional. Multiplier for adjusting the default learning rate.

    -
    - -
    -
    -field tuning_task: Optional[TuningTask] = None (alias 'tuningTask')
    -

    Optional. The tuning task. Either I2V or T2V.

    -
    - -

    - -
    -
    -class genai.types.VeoHyperParametersDict
    -

    Bases: TypedDict

    -

    Hyperparameters for Veo.

    -
    -
    -epoch_count: Optional[int]
    -

    Optional. Number of complete passes the model makes over the entire training dataset during training.

    -
    - -
    -
    -learning_rate_multiplier: Optional[float]
    -

    Optional. Multiplier for adjusting the default learning rate.

    -
    - -
    -
    -tuning_task: Optional[TuningTask]
    -

    Optional. The tuning task. Either I2V or T2V.

    -
    - -
    - -
    -
    -pydantic model genai.types.VeoTuningSpec
    -

    Bases: BaseModel

    -

    Tuning Spec for Veo Model Tuning.

    -

    Create a new model by parsing and validating input data from keyword arguments.

    -

    Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be -validated to form a valid model.

    -

    self is explicitly positional-only to allow self as a field name.

    -

    -Show JSON schema
    {
-   "title": "VeoTuningSpec",
-   "description": "Tuning Spec for Veo Model Tuning.",
-   "type": "object",
-   "properties": {
-      "hyperParameters": {
-         "anyOf": [
-            {
-               "$ref": "#/$defs/VeoHyperParameters"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "Optional. Hyperparameters for Veo."
-      },
-      "trainingDatasetUri": {
-         "anyOf": [
-            {
-               "type": "string"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "Required. Training dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.",
-         "title": "Trainingdataseturi"
-      },
-      "validationDatasetUri": {
-         "anyOf": [
-            {
-               "type": "string"
-            },
-            {
-               "type": "null"
-            }
-         ],
-         "default": null,
-         "description": "Optional. Validation dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.",
-         "title": "Validationdataseturi"
-      }
-   },
-   "$defs": {
-      "TuningTask": {
-         "description": "Optional. The tuning task. Either I2V or T2V.",
-         "enum": [
-            "TUNING_TASK_UNSPECIFIED",
-            "TUNING_TASK_I2V",
-            "TUNING_TASK_T2V"
-         ],
-         "title": "TuningTask",
-         "type": "string"
-      },
-      "VeoHyperParameters": {
-         "additionalProperties": false,
-         "description": "Hyperparameters for Veo.",
-         "properties": {
-            "epochCount": {
-               "anyOf": [
-                  {
-                     "type": "integer"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Number of complete passes the model makes over the entire training dataset during training.",
-               "title": "Epochcount"
-            },
-            "learningRateMultiplier": {
-               "anyOf": [
-                  {
-                     "type": "number"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. Multiplier for adjusting the default learning rate.",
-               "title": "Learningratemultiplier"
-            },
-            "tuningTask": {
-               "anyOf": [
-                  {
-                     "$ref": "#/$defs/TuningTask"
-                  },
-                  {
-                     "type": "null"
-                  }
-               ],
-               "default": null,
-               "description": "Optional. The tuning task. Either I2V or T2V."
-            }
-         },
-         "title": "VeoHyperParameters",
-         "type": "object"
-      }
-   },
-   "additionalProperties": false
-}
-
    -
    -

    -
    Fields:
    -
    -
    -
    -
    -
    -field hyper_parameters: Optional[VeoHyperParameters] = None (alias 'hyperParameters')
    -

    Optional. Hyperparameters for Veo.

    -
    - -
    -
    -field training_dataset_uri: Optional[str] = None (alias 'trainingDatasetUri')
    -

    Required. Training dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.

    -
    - -
    -
    -field validation_dataset_uri: Optional[str] = None (alias 'validationDatasetUri')
    -

    Optional. Validation dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.

    -
    - -
    - -
    -
    -class genai.types.VeoTuningSpecDict
    -

    Bases: TypedDict

    -

    Tuning Spec for Veo Model Tuning.

    -
    -
    -hyper_parameters: Optional[VeoHyperParametersDict]
    -

    Optional. Hyperparameters for Veo.

    -
    - -
    -
    -training_dataset_uri: Optional[str]
    -

    Required. Training dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.

    -
    - -
    -
    -validation_dataset_uri: Optional[str]
    -

    Optional. Validation dataset used for tuning. The dataset can be specified as either a Cloud Storage path to a JSONL file or as the resource name of a Vertex Multimodal Dataset.

    -
    - -
    -
    pydantic model genai.types.VertexAISearch
    diff --git a/docs/genindex.html b/docs/genindex.html index 9d922a223..c9b29da99 100644 --- a/docs/genindex.html +++ b/docs/genindex.html @@ -318,14 +318,6 @@

    A

    - - + - - + +
  • candidate_response_field_name (genai.types.PairwiseMetricSpec attribute) + +
  • CandidateDict (class in genai.types) @@ -1128,14 +1150,20 @@

    C

  • (genai.types.GenerateVideosConfigDict attribute)
    • - - + +
  • custom_function (genai.types.Metric attribute) + +
    • +
  • custom_output_format_config (genai.types.PairwiseMetricSpec attribute) + +
    • +
  • CustomOutputFormatConfigDict (class in genai.types) +
    • @@ -1598,24 +1650,20 @@

    D

    -
  • distillation_spec (genai.types.TuningJob attribute) - -
  • DistillationDataStatsDict (class in genai.types) -
    • -
  • DistillationHyperParametersDict (class in genai.types) -
    • -
  • DistillationSpecDict (class in genai.types)
  • DIVERSITY (genai.types.MusicGenerationMode attribute)
  • DOCUMENT (genai.types.MediaModality attribute)
    • +
  • document_name (genai.types.GroundingChunkRetrievedContext attribute) + +
  • domain (genai.types.GroundingChunkWeb attribute)
    • - - + +
  • Environment (class in genai.types) +
    • +
  • environment (genai.types.ToolComputerUse attribute) + +
    • +
  • ENVIRONMENT_BROWSER (genai.types.Environment attribute) +
    • +
  • ENVIRONMENT_UNSPECIFIED (genai.types.Environment attribute) +
  • epoch (genai.types.Checkpoint attribute)
  • es (genai.types.ImagePromptLanguage attribute) +
    • +
  • evaluation_config (genai.types.CreateTuningJobConfig attribute) + +
    • +
  • EvaluationConfigDict (class in genai.types)
  • example (genai.types.Schema attribute) @@ -1976,6 +2038,16 @@

    E

    • +
  • exclude_domains (genai.types.EnterpriseWebSearch attribute) + +
  • executable_code (genai.types.GenerateContentResponse property) @@ -2198,6 +2270,14 @@

    F

  • GeneratedImageDict (class in genai.types) +
    • +
  • GeneratedImageMaskDict (class in genai.types)
  • GeneratedVideoDict (class in genai.types)
    • @@ -2508,6 +2604,8 @@

    G

  • (genai.types.LiveServerContentDict attribute)
    • + + - -
  • hyper_parameters (genai.types.DistillationSpec attribute) +
  • hyper_parameters (genai.types.PartnerModelTuningSpec attribute)
    • @@ -3034,9 +3122,17 @@

    I

  • (genai.types.Modality attribute)
    • -
  • image (genai.types.UpscaleImageParameters attribute) +
  • image (genai.types.ScribbleImage attribute)
    • @@ -3090,6 +3186,8 @@

    I

  • (genai.types.UpscaleImageConfigDict attribute)
    • + + - - + @@ -3280,10 +3384,20 @@

    K

    L

    - + + - - + + - @@ -6219,6 +6441,8 @@

    T

  • (genai.types.SchemaDict attribute)
    • +
  • to_yaml_file() (genai.types.Metric method) +
  • token (genai.types.LogprobsResultCandidate attribute)
      @@ -6316,6 +6540,8 @@

      T

  • ToolCodeExecutionDict (class in genai.types) +
    • +
  • ToolComputerUseDict (class in genai.types)
  • ToolConfigDict (class in genai.types)
    • @@ -6343,6 +6569,8 @@

    T

  • (genai.types.LiveConnectConfigDict attribute)
    • + + - - +
    +
  • max_predictions (genai.types.SegmentImageConfig attribute) + +
  • max_properties (genai.types.JSONSchema attribute) @@ -3796,6 +3914,24 @@

    M

  • (genai.types.SafetySetting attribute)
  • (genai.types.SafetySettingDict attribute) +
    • + +
  • metric_prompt_template (genai.types.PairwiseMetricSpec attribute) + +
    • +
  • MetricDict (class in genai.types) +
    • +
  • metrics (genai.types.EvaluationConfig attribute) + +
  • mime_type (genai.types.AudioChunk attribute) @@ -3832,6 +3968,8 @@

    M

  • (genai.types.VideoDict attribute)
    • +
  • model_post_init() (genai.types.Image method) + +
  • model_routing_preference (genai.types.GenerationConfigRoutingConfigAutoRoutingMode attribute)
    		• + + -
  • tuning_task (genai.types.VeoHyperParameters attribute) - -
    • -
  • TUNING_TASK_I2V (genai.types.TuningTask attribute) -
    • -
  • TUNING_TASK_T2V (genai.types.TuningTask attribute) -
    • -
  • TUNING_TASK_UNSPECIFIED (genai.types.TuningTask attribute) -
  • TuningDatasetDict (class in genai.types)
  • TuningDataStatsDict (class in genai.types) @@ -6657,8 +6853,6 @@

    T

  • (genai.client.Client property)
    • -
  • TuningTask (class in genai.types) -
  • TuningValidationDatasetDict (class in genai.types)
  • turn_complete (genai.types.LiveClientContent attribute) @@ -6821,8 +7015,6 @@

    U

  • (genai.types.ToolDict attribute)
    • -

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zy~bR|B@dASx1QXc>b~rG|B~th@jp!|{ErrHf5|xX+QOJ7ZG`a81)n#3$J!)9C4{Ey zy7|JLAt|2f9&=Ws<5CsJIm_~xn62&IC{s2a-7i5k9noA<-dM6!ASj1@Id@?_-YVdI z;@`dE(arvWw4vCv|8NgloNr82RA+*enC1#K8m|q(*k2w%S-@~qH60~U?PFn{W_rG) VMw4^Vo}&7wl4hQ3aP+^q{{w0Lcy0gy literal 0 HcmV?d00001 diff --git a/google/genai/tests/models/test_segment_image.py b/google/genai/tests/models/test_segment_image.py new file mode 100644 index 000000000..495d52e56 --- /dev/null +++ b/google/genai/tests/models/test_segment_image.py @@ -0,0 +1,147 @@ +# Copyright 2025 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + + +"""Tests for segment_image.""" + +import os + +import pytest + +from ... import types +from .. import pytest_helper + +SEGMENT_IMAGE_MODEL_LATEST = 'image-segmentation-001' + +SOURCE_IMAGE_FILE_PATH1 = os.path.abspath( + os.path.join(os.path.dirname(__file__), '../data/google.png') +) + +SOURCE_IMAGE1 = types.Image.from_file(location=SOURCE_IMAGE_FILE_PATH1) + +SOURCE_IMAGE_FILE_PATH2 = os.path.abspath( + os.path.join(os.path.dirname(__file__), '../data/skateboard_stop_sign.jpg') +) + +SOURCE_IMAGE2 = types.Image.from_file(location=SOURCE_IMAGE_FILE_PATH2) + +SCRIBBLE_IMAGE_FILE_PATH = os.path.abspath( + os.path.join(os.path.dirname(__file__), '../data/segmentation_scribble.jpg') +) + +SCRIBBLE_IMAGE = types.Image.from_file(location=SCRIBBLE_IMAGE_FILE_PATH) + +test_table: list[pytest_helper.TestTableItem] = [ + pytest_helper.TestTableItem( + name='test_segment_foreground', + exception_if_mldev='only supported in the Vertex AI client', + parameters=types._SegmentImageParameters( + model=SEGMENT_IMAGE_MODEL_LATEST, + source=types.SegmentImageSource( + image=SOURCE_IMAGE1, + ), + config=types.SegmentImageConfig( + mode=types.SegmentMode.FOREGROUND, + max_predictions=1, + confidence_threshold=0.02, + mask_dilation=0.02, + binary_color_threshold=98, + ), + ), + ), + pytest_helper.TestTableItem( + name='test_segment_background', + exception_if_mldev='only supported in the Vertex AI client', + parameters=types._SegmentImageParameters( + model=SEGMENT_IMAGE_MODEL_LATEST, + source=types.SegmentImageSource( + image=SOURCE_IMAGE1, + ), + config=types.SegmentImageConfig( + mode=types.SegmentMode.BACKGROUND, + ), + ), + ), + pytest_helper.TestTableItem( + name='test_segment_prompt', + exception_if_mldev='only supported in the Vertex AI client', + parameters=types._SegmentImageParameters( + model=SEGMENT_IMAGE_MODEL_LATEST, + source=types.SegmentImageSource( + image=SOURCE_IMAGE1, + prompt='The letter G', + ), + config=types.SegmentImageConfig( + mode=types.SegmentMode.PROMPT, + ), + ), + ), + pytest_helper.TestTableItem( + name='test_segment_semantic', + exception_if_mldev='only supported in the Vertex AI client', + parameters=types._SegmentImageParameters( + model=SEGMENT_IMAGE_MODEL_LATEST, + source=types.SegmentImageSource( + image=SOURCE_IMAGE2, + prompt='skateboard', + ), + config=types.SegmentImageConfig( + mode=types.SegmentMode.SEMANTIC, + ), + ), + ), + pytest_helper.TestTableItem( + name='test_segment_interactive', + exception_if_mldev='only supported in the Vertex AI client', + parameters=types._SegmentImageParameters( + model=SEGMENT_IMAGE_MODEL_LATEST, + source=types.SegmentImageSource( + image=SOURCE_IMAGE2, + scribble_image=types.ScribbleImage(image=SCRIBBLE_IMAGE), + ), + config=types.SegmentImageConfig( + mode=types.SegmentMode.INTERACTIVE, + ), + ), + ), +] +pytestmark = pytest_helper.setup( + file=__file__, + globals_for_file=globals(), + test_method='models.segment_image', + test_table=test_table, +) + + +@pytest.mark.asyncio +async def test_segment_foreground_async(client): + with pytest_helper.exception_if_mldev(client, ValueError): + response = await client.aio.models.segment_image( + model=SEGMENT_IMAGE_MODEL_LATEST, + source=types.SegmentImageSource( + image=SOURCE_IMAGE2, + ), + config=types.SegmentImageConfig( + mode=types.SegmentMode.FOREGROUND, + max_predictions=1, + confidence_threshold=0.02, + mask_dilation=0.02, + binary_color_threshold=98, + ) + ) + assert response.generated_masks[0].mask.image_bytes + assert len(response.generated_masks) == 1 + assert response.generated_masks[0].labels[0].label == 'foreground' + assert response.generated_masks[0].labels[0].score > 0 diff --git a/google/genai/types.py b/google/genai/types.py index c2d7cf67b..f8e03bf4c 100644 --- a/google/genai/types.py +++ b/google/genai/types.py @@ -564,6 +564,16 @@ class EditMode(_common.CaseInSensitiveEnum): EDIT_MODE_PRODUCT_IMAGE = 'EDIT_MODE_PRODUCT_IMAGE' +class SegmentMode(_common.CaseInSensitiveEnum): + """Enum that represents the segmentation mode.""" + + FOREGROUND = 'FOREGROUND' + BACKGROUND = 'BACKGROUND' + PROMPT = 'PROMPT' + SEMANTIC = 'SEMANTIC' + INTERACTIVE = 'INTERACTIVE' + + class VideoCompressionQuality(_common.CaseInSensitiveEnum): """Enum that controls the compression quality of the generated videos.""" @@ -7241,6 +7251,236 @@ class RecontextImageResponseDict(TypedDict, total=False): ] +class ScribbleImage(_common.BaseModel): + """An image mask representing a brush scribble.""" + + image: Optional[Image] = Field( + default=None, + description="""The brush scribble to guide segmentation. Valid for the interactive mode.""", + ) + + +class ScribbleImageDict(TypedDict, total=False): + """An image mask representing a brush scribble.""" + + image: Optional[ImageDict] + """The brush scribble to guide segmentation. Valid for the interactive mode.""" + + +ScribbleImageOrDict = Union[ScribbleImage, ScribbleImageDict] + + +class SegmentImageSource(_common.BaseModel): + """A set of source input(s) for image segmentation.""" + + prompt: Optional[str] = Field( + default=None, + description="""A text prompt for guiding the model during image segmentation. + Required for prompt mode and semantic mode, disallowed for other modes.""", + ) + image: Optional[Image] = Field( + default=None, description="""The image to be segmented.""" + ) + scribble_image: Optional[ScribbleImage] = Field( + default=None, + description="""The brush scribble to guide segmentation. + Required for the interactive mode, disallowed for other modes.""", + ) + + +class SegmentImageSourceDict(TypedDict, total=False): + """A set of source input(s) for image segmentation.""" + + prompt: Optional[str] + """A text prompt for guiding the model during image segmentation. + Required for prompt mode and semantic mode, disallowed for other modes.""" + + image: Optional[ImageDict] + """The image to be segmented.""" + + scribble_image: Optional[ScribbleImageDict] + """The brush scribble to guide segmentation. + Required for the interactive mode, disallowed for other modes.""" + + +SegmentImageSourceOrDict = Union[SegmentImageSource, SegmentImageSourceDict] + + +class SegmentImageConfig(_common.BaseModel): + """Configuration for segmenting an image.""" + + http_options: Optional[HttpOptions] = Field( + default=None, description="""Used to override HTTP request options.""" + ) + mode: Optional[SegmentMode] = Field( + default=None, description="""The segmentation mode to use.""" + ) + max_predictions: Optional[int] = Field( + default=None, + description="""The maximum number of predictions to return up to, by top + confidence score.""", + ) + confidence_threshold: Optional[float] = Field( + default=None, + description="""The confidence score threshold for the detections as a decimal + value. Only predictions with a confidence score higher than this + threshold will be returned.""", + ) + mask_dilation: Optional[float] = Field( + default=None, + description="""A decimal value representing how much dilation to apply to the + masks. 0 for no dilation. 1.0 means the masked area covers the whole + image.""", + ) + binary_color_threshold: Optional[float] = Field( + default=None, + description="""The binary color threshold to apply to the masks. The threshold + can be set to a decimal value between 0 and 255 non-inclusive. + Set to -1 for no binary color thresholding.""", + ) + + +class SegmentImageConfigDict(TypedDict, total=False): + """Configuration for segmenting an image.""" + + http_options: Optional[HttpOptionsDict] + """Used to override HTTP request options.""" + + mode: Optional[SegmentMode] + """The segmentation mode to use.""" + + max_predictions: Optional[int] + """The maximum number of predictions to return up to, by top + confidence score.""" + + confidence_threshold: Optional[float] + """The confidence score threshold for the detections as a decimal + value. Only predictions with a confidence score higher than this + threshold will be returned.""" + + mask_dilation: Optional[float] + """A decimal value representing how much dilation to apply to the + masks. 0 for no dilation. 1.0 means the masked area covers the whole + image.""" + + binary_color_threshold: Optional[float] + """The binary color threshold to apply to the masks. The threshold + can be set to a decimal value between 0 and 255 non-inclusive. + Set to -1 for no binary color thresholding.""" + + +SegmentImageConfigOrDict = Union[SegmentImageConfig, SegmentImageConfigDict] + + +class _SegmentImageParameters(_common.BaseModel): + """The parameters for segmenting an image.""" + + model: Optional[str] = Field( + default=None, + description="""ID of the model to use. For a list of models, see `Google models + `_.""", + ) + source: Optional[SegmentImageSource] = Field( + default=None, + description="""A set of source input(s) for image segmentation.""", + ) + config: Optional[SegmentImageConfig] = Field( + default=None, description="""Configuration for image segmentation.""" + ) + + +class _SegmentImageParametersDict(TypedDict, total=False): + """The parameters for segmenting an image.""" + + model: Optional[str] + """ID of the model to use. For a list of models, see `Google models + `_.""" + + source: Optional[SegmentImageSourceDict] + """A set of source input(s) for image segmentation.""" + + config: Optional[SegmentImageConfigDict] + """Configuration for image segmentation.""" + + +_SegmentImageParametersOrDict = Union[ + _SegmentImageParameters, _SegmentImageParametersDict +] + + +class EntityLabel(_common.BaseModel): + """An entity representing the segmented area.""" + + label: Optional[str] = Field( + default=None, description="""The label of the segmented entity.""" + ) + score: Optional[float] = Field( + default=None, + description="""The confidence score of the detected label.""", + ) + + +class EntityLabelDict(TypedDict, total=False): + """An entity representing the segmented area.""" + + label: Optional[str] + """The label of the segmented entity.""" + + score: Optional[float] + """The confidence score of the detected label.""" + + +EntityLabelOrDict = Union[EntityLabel, EntityLabelDict] + + +class GeneratedImageMask(_common.BaseModel): + """A generated image mask.""" + + mask: Optional[Image] = Field( + default=None, description="""The generated image mask.""" + ) + labels: Optional[list[EntityLabel]] = Field( + default=None, + description="""The detected entities on the segmented area.""", + ) + + +class GeneratedImageMaskDict(TypedDict, total=False): + """A generated image mask.""" + + mask: Optional[ImageDict] + """The generated image mask.""" + + labels: Optional[list[EntityLabelDict]] + """The detected entities on the segmented area.""" + + +GeneratedImageMaskOrDict = Union[GeneratedImageMask, GeneratedImageMaskDict] + + +class SegmentImageResponse(_common.BaseModel): + """The output images response.""" + + generated_masks: Optional[list[GeneratedImageMask]] = Field( + default=None, + description="""List of generated image masks. + """, + ) + + +class SegmentImageResponseDict(TypedDict, total=False): + """The output images response.""" + + generated_masks: Optional[list[GeneratedImageMaskDict]] + """List of generated image masks. + """ + + +SegmentImageResponseOrDict = Union[ + SegmentImageResponse, SegmentImageResponseDict +] + + class GetModelConfig(_common.BaseModel): """Optional parameters for models.get method.""" From 3abf4414002cde35c8f6d023271b12577bc98016 Mon Sep 17 00:00:00 2001 From: Amy Wu Date: Thu, 14 Aug 2025 16:41:44 -0700 Subject: [PATCH 2/9] fix: Fix the bug to support Gemini Batch inlined requests system instruction PiperOrigin-RevId: 795249849 --- google/genai/batches.py | 140 ++++++++++++++++++ .../test_create_with_inlined_requests.py | 18 ++- 2 files changed, 157 insertions(+), 1 deletion(-) diff --git a/google/genai/batches.py b/google/genai/batches.py index 3a56b9314..811c15eef 100644 --- a/google/genai/batches.py +++ b/google/genai/batches.py @@ -2257,6 +2257,17 @@ def create( ) print(batch_job.state) """ + parameter_model = types._CreateBatchJobParameters( + 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: if isinstance(src, list): raise ValueError( @@ -2265,6 +2276,65 @@ def create( ) config = _extra_utils.format_destination(src, config) + else: + if isinstance(parameter_model.src, list) or ( + not isinstance(parameter_model.src, str) + and parameter_model.src + and parameter_model.src.inlined_requests + ): + # Handle system instruction in InlinedRequests. + request_url_dict: Optional[dict[str, str]] + request_dict: dict[str, Any] = _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)}' + request_dict.pop('config', None) + + request_dict = _common.convert_to_dict(request_dict) + request_dict = _common.encode_unserializable_types(request_dict) + # Move system instruction to 'request': + # {'systemInstruction': system_instruction} + requests = [] + batch_dict = request_dict.get('batch') + if batch_dict and isinstance(batch_dict, dict): + input_config_dict = batch_dict.get('inputConfig') + if input_config_dict and isinstance(input_config_dict, dict): + requests_dict = input_config_dict.get('requests') + if requests_dict and isinstance(requests_dict, dict): + requests = requests_dict.get('requests') + new_requests = [] + if requests: + for req in requests: + if req.get('systemInstruction'): + value = req.pop('systemInstruction') + req['request'].update({'systemInstruction': value}) + new_requests.append(req) + request_dict['batch']['inputConfig']['requests'][ # type: ignore + 'requests' + ] = new_requests + + response = self._api_client.request( + 'post', path, request_dict, http_options + ) + + response_dict = '' if not response.body else json.loads(response.body) + + response_dict = _BatchJob_from_mldev(response_dict) + + return_value = types.BatchJob._from_response( + response=response_dict, kwargs=parameter_model.model_dump() + ) + + self._api_client._verify_response(return_value) + return return_value + return self._create(model=model, src=src, config=config) def list( @@ -2691,6 +2761,17 @@ async def create( src="gs://path/to/input/data", ) """ + parameter_model = types._CreateBatchJobParameters( + 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: if isinstance(src, list): raise ValueError( @@ -2699,6 +2780,65 @@ async def create( ) config = _extra_utils.format_destination(src, config) + else: + if isinstance(parameter_model.src, list) or ( + not isinstance(parameter_model.src, str) + and parameter_model.src + and parameter_model.src.inlined_requests + ): + # Handle system instruction in InlinedRequests. + request_url_dict: Optional[dict[str, str]] + request_dict: dict[str, Any] = _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)}' + request_dict.pop('config', None) + + request_dict = _common.convert_to_dict(request_dict) + request_dict = _common.encode_unserializable_types(request_dict) + # Move system instruction to 'request': + # {'systemInstruction': system_instruction} + requests = [] + batch_dict = request_dict.get('batch') + if batch_dict and isinstance(batch_dict, dict): + input_config_dict = batch_dict.get('inputConfig') + if input_config_dict and isinstance(input_config_dict, dict): + requests_dict = input_config_dict.get('requests') + if requests_dict and isinstance(requests_dict, dict): + requests = requests_dict.get('requests') + new_requests = [] + if requests: + for req in requests: + if req.get('systemInstruction'): + value = req.pop('systemInstruction') + req['request'].update({'systemInstruction': value}) + new_requests.append(req) + request_dict['batch']['inputConfig']['requests'][ # type: ignore + 'requests' + ] = new_requests + + response = await self._api_client.async_request( + 'post', path, request_dict, http_options + ) + + response_dict = '' if not response.body else json.loads(response.body) + + response_dict = _BatchJob_from_mldev(response_dict) + + return_value = types.BatchJob._from_response( + response=response_dict, kwargs=parameter_model.model_dump() + ) + + self._api_client._verify_response(return_value) + return return_value + return await self._create(model=model, src=src, config=config) async def list( diff --git a/google/genai/tests/batches/test_create_with_inlined_requests.py b/google/genai/tests/batches/test_create_with_inlined_requests.py index 3faf8c40f..980bb0cee 100644 --- a/google/genai/tests/batches/test_create_with_inlined_requests.py +++ b/google/genai/tests/batches/test_create_with_inlined_requests.py @@ -39,12 +39,17 @@ _INLINED_TEXT_REQUEST = { 'contents': [{ 'parts': [{ - 'text': 'What is the QQQ stock price?', + 'text': 'high', }], 'role': 'user', }], 'config': { 'response_modalities': ['TEXT'], + 'system_instruction': 'I say high, you say low', + 'thinking_config': { + 'include_thoughts': True, + 'thinking_budget': 4000, + }, }, } _INLINED_IMAGE_REQUEST = { @@ -130,6 +135,17 @@ ), pytest_helper.TestTableItem( name='test_with_inlined_request', + parameters=types._CreateBatchJobParameters( + model=_MLDEV_GEMINI_MODEL, + src={'inlined_requests': [_INLINED_REQUEST]}, + config={ + 'display_name': _DISPLAY_NAME, + }, + ), + exception_if_vertex='not supported', + ), + pytest_helper.TestTableItem( + name='test_union_with_inlined_request_system_instruction', parameters=types._CreateBatchJobParameters( model=_MLDEV_GEMINI_MODEL, src={'inlined_requests': [_INLINED_TEXT_REQUEST]}, From 91f3ed7d1516a1850f89934a7694cf3668b81c51 Mon Sep 17 00:00:00 2001 From: Qiao Wang Date: Fri, 15 Aug 2025 11:00:03 -0700 Subject: [PATCH 3/9] chore: Add `hasUnion` flag to a batch creation test case PiperOrigin-RevId: 795535359 --- google/genai/tests/batches/test_create_with_inlined_requests.py | 1 + 1 file changed, 1 insertion(+) diff --git a/google/genai/tests/batches/test_create_with_inlined_requests.py b/google/genai/tests/batches/test_create_with_inlined_requests.py index 980bb0cee..67455a5e2 100644 --- a/google/genai/tests/batches/test_create_with_inlined_requests.py +++ b/google/genai/tests/batches/test_create_with_inlined_requests.py @@ -153,6 +153,7 @@ 'display_name': _DISPLAY_NAME, }, ), + has_union=True, exception_if_vertex='not supported', ), pytest_helper.TestTableItem( From 26a7b62bf786498315287a7f4619695c3d68a495 Mon Sep 17 00:00:00 2001 From: Amy Wu Date: Fri, 15 Aug 2025 15:08:45 -0700 Subject: [PATCH 4/9] chore: internal change PiperOrigin-RevId: 795628626 --- google/genai/_api_client.py | 64 +++++++++---------- .../genai/_automatic_function_calling_util.py | 12 ++++ google/genai/_live_converters.py | 1 + google/genai/_tokens_converters.py | 1 + google/genai/batches.py | 1 + google/genai/caches.py | 1 + google/genai/files.py | 1 + google/genai/models.py | 1 + google/genai/operations.py | 1 + .../client/test_client_initialization.py | 21 +++--- .../tests/client/test_client_requests.py | 14 ++-- .../genai/tests/client/test_http_options.py | 6 +- google/genai/tests/client/test_retries.py | 8 +-- google/genai/tests/conftest.py | 6 ++ google/genai/tests/tokens/test_create.py | 14 ++-- google/genai/tunings.py | 1 + 16 files changed, 90 insertions(+), 63 deletions(-) diff --git a/google/genai/_api_client.py b/google/genai/_api_client.py index f46534bca..021cedbfd 100644 --- a/google/genai/_api_client.py +++ b/google/genai/_api_client.py @@ -91,7 +91,7 @@ class EphemeralTokenAPIKeyError(ValueError): # This method checks for the API key in the environment variables. Google API # key is precedenced over Gemini API key. -def _get_env_api_key() -> Optional[str]: +def get_env_api_key() -> Optional[str]: """Gets the API key from environment variables, prioritizing GOOGLE_API_KEY. Returns: @@ -108,7 +108,7 @@ def _get_env_api_key() -> Optional[str]: return env_google_api_key or env_gemini_api_key or None -def _append_library_version_headers(headers: dict[str, str]) -> None: +def append_library_version_headers(headers: dict[str, str]) -> None: """Appends the telemetry header to the headers dict.""" library_label = f'google-genai-sdk/{version.__version__}' language_label = 'gl-python/' + sys.version.split()[0] @@ -131,7 +131,7 @@ def _append_library_version_headers(headers: dict[str, str]) -> None: headers['x-goog-api-client'] = version_header_value -def _patch_http_options( +def patch_http_options( options: HttpOptions, patch_options: HttpOptions ) -> HttpOptions: copy_option = options.model_copy() @@ -155,11 +155,11 @@ def _patch_http_options( setattr(copy_option, key, getattr(options, key)) if copy_option.headers is not None: - _append_library_version_headers(copy_option.headers) + append_library_version_headers(copy_option.headers) return copy_option -def _populate_server_timeout_header( +def populate_server_timeout_header( headers: dict[str, str], timeout_in_seconds: Optional[Union[float, int]] ) -> None: """Populates the server timeout header in the headers dict.""" @@ -167,7 +167,7 @@ def _populate_server_timeout_header( headers['X-Server-Timeout'] = str(math.ceil(timeout_in_seconds)) -def _join_url_path(base_url: str, path: str) -> str: +def join_url_path(base_url: str, path: str) -> str: parsed_base = urlparse(base_url) base_path = ( parsed_base.path[:-1] @@ -178,7 +178,7 @@ def _join_url_path(base_url: str, path: str) -> str: return urlunparse(parsed_base._replace(path=base_path + '/' + path)) -def _load_auth(*, project: Union[str, None]) -> Tuple[Credentials, str]: +def load_auth(*, project: Union[str, None]) -> Tuple[Credentials, str]: """Loads google auth credentials and project id.""" credentials, loaded_project_id = google.auth.default( # type: ignore[no-untyped-call] scopes=['https://www.googleapis.com/auth/cloud-platform'], @@ -195,12 +195,12 @@ def _load_auth(*, project: Union[str, None]) -> Tuple[Credentials, str]: return credentials, project -def _refresh_auth(credentials: Credentials) -> Credentials: +def refresh_auth(credentials: Credentials) -> Credentials: credentials.refresh(Request()) # type: ignore[no-untyped-call] return credentials -def _get_timeout_in_seconds( +def get_timeout_in_seconds( timeout: Optional[Union[float, int]], ) -> Optional[float]: """Converts the timeout to seconds.""" @@ -454,7 +454,7 @@ def _load_json_from_response(cls, response: Any) -> Any: ) -def _retry_args(options: Optional[HttpRetryOptions]) -> _common.StringDict: +def retry_args(options: Optional[HttpRetryOptions]) -> _common.StringDict: """Returns the retry args for the given http retry options. Args: @@ -574,7 +574,7 @@ def __init__( # Retrieve implicitly set values from the environment. env_project = os.environ.get('GOOGLE_CLOUD_PROJECT', None) env_location = os.environ.get('GOOGLE_CLOUD_LOCATION', None) - env_api_key = _get_env_api_key() + env_api_key = get_env_api_key() self.project = project or env_project self.location = location or env_location self.api_key = api_key or env_api_key @@ -631,7 +631,7 @@ def __init__( and not self.api_key and not validated_http_options.base_url ): - credentials, self.project = _load_auth(project=None) + credentials, self.project = load_auth(project=None) if not self._credentials: self._credentials = credentials @@ -670,12 +670,12 @@ def __init__( self._http_options.headers['x-goog-api-key'] = self.api_key # Update the http options with the user provided http options. if http_options: - self._http_options = _patch_http_options( + self._http_options = patch_http_options( self._http_options, validated_http_options ) else: if self._http_options.headers is not None: - _append_library_version_headers(self._http_options.headers) + append_library_version_headers(self._http_options.headers) client_args, async_client_args = self._ensure_httpx_ssl_ctx( self._http_options @@ -689,7 +689,7 @@ def __init__( ) self._websocket_ssl_ctx = self._ensure_websocket_ssl_ctx(self._http_options) - retry_kwargs = _retry_args(self._http_options.retry_options) + retry_kwargs = retry_args(self._http_options.retry_options) self._retry = tenacity.Retrying(**retry_kwargs) self._async_retry = tenacity.AsyncRetrying(**retry_kwargs) @@ -889,14 +889,14 @@ def _access_token(self) -> str: """Retrieves the access token for the credentials.""" with self._sync_auth_lock: if not self._credentials: - self._credentials, project = _load_auth(project=self.project) + self._credentials, project = load_auth(project=self.project) if not self.project: self.project = project if self._credentials: if self._credentials.expired or not self._credentials.token: # Only refresh when it needs to. Default expiration is 3600 seconds. - _refresh_auth(self._credentials) + refresh_auth(self._credentials) if not self._credentials.token: raise RuntimeError('Could not resolve API token from the environment') return self._credentials.token # type: ignore[no-any-return] @@ -912,7 +912,7 @@ async def _async_access_token(self) -> Union[str, Any]: if not self._credentials: # Double check that the credentials are not set before loading them. self._credentials, project = await asyncio.to_thread( - _load_auth, project=self.project + load_auth, project=self.project ) if not self.project: self.project = project @@ -923,7 +923,7 @@ async def _async_access_token(self) -> Union[str, Any]: async with self._async_auth_lock: if self._credentials.expired or not self._credentials.token: # Double check that the credentials expired before refreshing. - await asyncio.to_thread(_refresh_auth, self._credentials) + await asyncio.to_thread(refresh_auth, self._credentials) if not self._credentials.token: raise RuntimeError('Could not resolve API token from the environment') @@ -946,12 +946,12 @@ def _build_request( # patch the http options with the user provided settings. if http_options: if isinstance(http_options, HttpOptions): - patched_http_options = _patch_http_options( + patched_http_options = patch_http_options( self._http_options, http_options, ) else: - patched_http_options = _patch_http_options( + patched_http_options = patch_http_options( self._http_options, HttpOptions.model_validate(http_options) ) else: @@ -993,7 +993,7 @@ def _build_request( request_dict, patched_http_options.extra_body ) - url = _join_url_path( + url = join_url_path( base_url, versioned_path, ) @@ -1003,11 +1003,11 @@ def _build_request( 'Ephemeral tokens can only be used with the live API.' ) - timeout_in_seconds = _get_timeout_in_seconds(patched_http_options.timeout) + timeout_in_seconds = get_timeout_in_seconds(patched_http_options.timeout) if patched_http_options.headers is None: raise ValueError('Request headers must be set.') - _populate_server_timeout_header( + populate_server_timeout_header( patched_http_options.headers, timeout_in_seconds ) return HttpRequest( @@ -1079,7 +1079,7 @@ def _request( ) # Support per request retry options. if parameter_model.retry_options: - retry_kwargs = _retry_args(parameter_model.retry_options) + retry_kwargs = retry_args(parameter_model.retry_options) retry = tenacity.Retrying(**retry_kwargs) return retry(self._request_once, http_request, stream) # type: ignore[no-any-return] @@ -1239,7 +1239,7 @@ async def _async_request( ) # Support per request retry options. if parameter_model.retry_options: - retry_kwargs = _retry_args(parameter_model.retry_options) + retry_kwargs = retry_args(parameter_model.retry_options) retry = tenacity.AsyncRetrying(**retry_kwargs) return await retry(self._async_request_once, http_request, stream) # type: ignore[no-any-return] return await self._async_retry( # type: ignore[no-any-return] @@ -1398,13 +1398,13 @@ def _upload_fd( if isinstance(self._http_options, dict) else self._http_options.timeout ) - timeout_in_seconds = _get_timeout_in_seconds(timeout) + timeout_in_seconds = get_timeout_in_seconds(timeout) upload_headers = { 'X-Goog-Upload-Command': upload_command, 'X-Goog-Upload-Offset': str(offset), 'Content-Length': str(chunk_size), } - _populate_server_timeout_header(upload_headers, timeout_in_seconds) + populate_server_timeout_header(upload_headers, timeout_in_seconds) retry_count = 0 while retry_count < MAX_RETRY_COUNT: response = self._httpx_client.request( @@ -1558,13 +1558,13 @@ async def _async_upload_fd( if isinstance(self._http_options, dict) else self._http_options.timeout ) - timeout_in_seconds = _get_timeout_in_seconds(timeout) + timeout_in_seconds = get_timeout_in_seconds(timeout) upload_headers = { 'X-Goog-Upload-Command': upload_command, 'X-Goog-Upload-Offset': str(offset), 'Content-Length': str(chunk_size), } - _populate_server_timeout_header(upload_headers, timeout_in_seconds) + populate_server_timeout_header(upload_headers, timeout_in_seconds) retry_count = 0 response = None @@ -1634,13 +1634,13 @@ async def _async_upload_fd( if isinstance(self._http_options, dict) else self._http_options.timeout ) - timeout_in_seconds = _get_timeout_in_seconds(timeout) + timeout_in_seconds = get_timeout_in_seconds(timeout) upload_headers = { 'X-Goog-Upload-Command': upload_command, 'X-Goog-Upload-Offset': str(offset), 'Content-Length': str(chunk_size), } - _populate_server_timeout_header(upload_headers, timeout_in_seconds) + populate_server_timeout_header(upload_headers, timeout_in_seconds) retry_count = 0 client_response = None diff --git a/google/genai/_automatic_function_calling_util.py b/google/genai/_automatic_function_calling_util.py index 3a9ffbe7a..ec7f9a702 100644 --- a/google/genai/_automatic_function_calling_util.py +++ b/google/genai/_automatic_function_calling_util.py @@ -30,6 +30,18 @@ else: VersionedUnionType = typing._UnionGenericAlias # type: ignore[attr-defined] + +__all__ = [ + '_py_builtin_type_to_schema_type', + '_raise_for_unsupported_param', + '_handle_params_as_deferred_annotations', + '_add_unevaluated_items_to_fixed_len_tuple_schema', + '_is_builtin_primitive_or_compound', + '_is_default_value_compatible', + '_parse_schema_from_parameter', + '_get_required_fields', +] + _py_builtin_type_to_schema_type = { str: types.Type.STRING, int: types.Type.INTEGER, diff --git a/google/genai/_live_converters.py b/google/genai/_live_converters.py index 119c75b9a..5aac6d1c8 100644 --- a/google/genai/_live_converters.py +++ b/google/genai/_live_converters.py @@ -16,6 +16,7 @@ # Code generated by the Google Gen AI SDK generator DO NOT EDIT. from typing import Any, Optional, Union + from . import _transformers as t from ._api_client import BaseApiClient from ._common import get_value_by_path as getv diff --git a/google/genai/_tokens_converters.py b/google/genai/_tokens_converters.py index 74ed98acf..20d3c107a 100644 --- a/google/genai/_tokens_converters.py +++ b/google/genai/_tokens_converters.py @@ -16,6 +16,7 @@ # Code generated by the Google Gen AI SDK generator DO NOT EDIT. from typing import Any, Optional, Union + from . import _transformers as t from ._api_client import BaseApiClient from ._common import get_value_by_path as getv diff --git a/google/genai/batches.py b/google/genai/batches.py index 811c15eef..7f3559e43 100644 --- a/google/genai/batches.py +++ b/google/genai/batches.py @@ -30,6 +30,7 @@ from ._common import set_value_by_path as setv from .pagers import AsyncPager, Pager + logger = logging.getLogger('google_genai.batches') diff --git a/google/genai/caches.py b/google/genai/caches.py index d56c4f1c0..91538fab2 100644 --- a/google/genai/caches.py +++ b/google/genai/caches.py @@ -29,6 +29,7 @@ from ._common import set_value_by_path as setv from .pagers import AsyncPager, Pager + logger = logging.getLogger('google_genai.caches') diff --git a/google/genai/files.py b/google/genai/files.py index 7babdf151..e16050799 100644 --- a/google/genai/files.py +++ b/google/genai/files.py @@ -31,6 +31,7 @@ from ._common import set_value_by_path as setv from .pagers import AsyncPager, Pager + logger = logging.getLogger('google_genai.files') diff --git a/google/genai/models.py b/google/genai/models.py index e414e9d9b..ff43d03d8 100644 --- a/google/genai/models.py +++ b/google/genai/models.py @@ -32,6 +32,7 @@ from ._common import set_value_by_path as setv from .pagers import AsyncPager, Pager + logger = logging.getLogger('google_genai.models') diff --git a/google/genai/operations.py b/google/genai/operations.py index 796b002cf..71158c6f7 100644 --- a/google/genai/operations.py +++ b/google/genai/operations.py @@ -26,6 +26,7 @@ from ._common import get_value_by_path as getv from ._common import set_value_by_path as setv + logger = logging.getLogger('google_genai.operations') diff --git a/google/genai/tests/client/test_client_initialization.py b/google/genai/tests/client/test_client_initialization.py index b961d76d5..97dc0f0f0 100644 --- a/google/genai/tests/client/test_client_initialization.py +++ b/google/genai/tests/client/test_client_initialization.py @@ -30,6 +30,7 @@ from ... import _base_url as base_url from ... import _replay_api_client as replay_api_client from ... import Client +from ... import types @pytest.fixture(autouse=True) @@ -223,12 +224,12 @@ def test_constructor_with_invalid_http_options_key(): def test_constructor_with_http_options_as_pydantic_type(): - mldev_http_options = api_client.HttpOptions( + mldev_http_options = types.HttpOptions( api_version="v1", base_url="https://placeholder-fake-url.com/", headers={"X-Custom-Header": "custom_value"}, ) - vertexai_http_options = api_client.HttpOptions( + vertexai_http_options = types.HttpOptions( api_version="v1", base_url=( "https://{self.location}-aiplatform.googleapis.com/{{api_version}}/" @@ -751,7 +752,7 @@ def test_client_logs_to_logger_instance(monkeypatch, caplog): def test_client_ssl_context_implicit_initialization(): client_args, async_client_args = ( - api_client.BaseApiClient._ensure_httpx_ssl_ctx(api_client.HttpOptions()) + api_client.BaseApiClient._ensure_httpx_ssl_ctx(types.HttpOptions()) ) assert client_args["verify"] @@ -760,7 +761,7 @@ def test_client_ssl_context_implicit_initialization(): import aiohttp # pylint: disable=g-import-not-at-top async_client_args = api_client.BaseApiClient._ensure_aiohttp_ssl_ctx( - api_client.HttpOptions() + types.HttpOptions() ) assert async_client_args["ssl"] assert isinstance(async_client_args["ssl"], ssl.SSLContext) @@ -775,7 +776,7 @@ def test_client_ssl_context_explicit_initialization_same_args(): capath=os.environ.get("SSL_CERT_DIR"), ) - options = api_client.HttpOptions( + options = types.HttpOptions( client_args={"verify": ctx}, async_client_args={"verify": ctx} ) client_args, async_client_args = ( @@ -807,7 +808,7 @@ def test_client_ssl_context_explicit_initialization_separate_args(): capath=os.environ.get("SSL_CERT_DIR"), ) - options = api_client.HttpOptions( + options = types.HttpOptions( client_args={"verify": ctx}, async_client_args={"verify": async_ctx} ) client_args, async_client_args = ( @@ -834,7 +835,7 @@ def test_client_ssl_context_explicit_initialization_sync_args(): capath=os.environ.get("SSL_CERT_DIR"), ) - options = api_client.HttpOptions(client_args={"verify": ctx}) + options = types.HttpOptions(client_args={"verify": ctx}) client_args, async_client_args = ( api_client.BaseApiClient._ensure_httpx_ssl_ctx(options) ) @@ -859,7 +860,7 @@ def test_client_ssl_context_explicit_initialization_async_args(): capath=os.environ.get("SSL_CERT_DIR"), ) - options = api_client.HttpOptions(async_client_args={"verify": ctx}) + options = types.HttpOptions(async_client_args={"verify": ctx}) client_args, async_client_args = ( api_client.BaseApiClient._ensure_httpx_ssl_ctx(options) ) @@ -1097,7 +1098,7 @@ def test_async_async_client_args_without_transport_allows_aiohttp_to_be_used(): vertexai=True, project="fake_project_id", location="fake-location", - http_options=api_client.HttpOptions(async_client_args={}), + http_options=types.HttpOptions(async_client_args={}), ) api_client.has_aiohttp = False @@ -1113,7 +1114,7 @@ def test_async_transport_forces_httpx_regardless_of_aiohttp_availability(): vertexai=True, project="fake_project_id", location="fake-location", - http_options=api_client.HttpOptions( + http_options=types.HttpOptions( async_client_args={"transport": httpx.AsyncBaseTransport()} ), ) diff --git a/google/genai/tests/client/test_client_requests.py b/google/genai/tests/client/test_client_requests.py index 03f2df9c5..e83fa1cb4 100644 --- a/google/genai/tests/client/test_client_requests.py +++ b/google/genai/tests/client/test_client_requests.py @@ -32,7 +32,7 @@ def test_join_url_path_base_url_with_trailing_slash_and_path_with_leading_slash( base_url = 'https://fake-url.com/some_path/' path = '/v1beta/models' assert ( - api_client._join_url_path(base_url, path) + api_client.join_url_path(base_url, path) == 'https://fake-url.com/some_path/v1beta/models' ) @@ -41,7 +41,7 @@ def test_join_url_path_with_base_url_with_trailing_slash_and_path_without_leadin base_url = 'https://fake-url.com/some_path/' path = 'v1beta/models' assert ( - api_client._join_url_path(base_url, path) + api_client.join_url_path(base_url, path) == 'https://fake-url.com/some_path/v1beta/models' ) @@ -50,7 +50,7 @@ def test_join_url_path_with_base_url_without_trailing_slash_and_path_with_leadin base_url = 'https://fake-url.com/some_path' path = '/v1beta/models' assert ( - api_client._join_url_path(base_url, path) + api_client.join_url_path(base_url, path) == 'https://fake-url.com/some_path/v1beta/models' ) @@ -59,7 +59,7 @@ def test_join_url_path_with_base_url_without_trailing_slash_and_path_without_lea base_url = 'https://fake-url.com/some_path' path = 'v1beta/models' assert ( - api_client._join_url_path(base_url, path) + api_client.join_url_path(base_url, path) == 'https://fake-url.com/some_path/v1beta/models' ) @@ -68,7 +68,7 @@ def test_join_url_path_base_url_without_path_with_trailing_slash(): base_url = 'https://fake-url.com/' path = 'v1beta/models' assert ( - api_client._join_url_path(base_url, path) + api_client.join_url_path(base_url, path) == 'https://fake-url.com/v1beta/models' ) @@ -77,7 +77,7 @@ def test_join_url_path_base_url_without_path_without_trailing_slash(): base_url = 'https://fake-url.com' path = 'v1beta/models' assert ( - api_client._join_url_path(base_url, path) + api_client.join_url_path(base_url, path) == 'https://fake-url.com/v1beta/models' ) @@ -129,7 +129,7 @@ def test_build_request_appends_to_goog_api_client_headers(monkeypatch): def test_build_request_keeps_sdk_version_headers(monkeypatch): headers_to_inject = {} - api_client._append_library_version_headers(headers_to_inject) + api_client.append_library_version_headers(headers_to_inject) assert 'google-genai-sdk/' in headers_to_inject['user-agent'] request_client = build_test_client(monkeypatch).models._api_client request = request_client._build_request( diff --git a/google/genai/tests/client/test_http_options.py b/google/genai/tests/client/test_http_options.py index 354d562ec..9b89892aa 100644 --- a/google/genai/tests/client/test_http_options.py +++ b/google/genai/tests/client/test_http_options.py @@ -33,7 +33,7 @@ def test_patch_http_options_with_copies_all_fields(): retry_options=types.HttpRetryOptions(attempts=10), ) options = types.HttpOptions() - patched = _api_client._patch_http_options(options, patch_options) + patched = _api_client.patch_http_options(options, patch_options) http_options_keys = types.HttpOptions.model_fields.keys() for key in http_options_keys: @@ -60,7 +60,7 @@ def test_patch_http_options_merges_headers(): headers={'X-Custom-Header': 'custom_value'}, timeout=10000, ) - patched = _api_client._patch_http_options(original_options, patch_options) + patched = _api_client.patch_http_options(original_options, patch_options) # If the header is present in both the original and patch options, the patch # options value should be used assert patched.headers['X-Custom-Header'] == 'custom_value' @@ -81,7 +81,7 @@ def test_patch_http_options_appends_version_headers(): headers={'X-Custom-Header': 'custom_value'}, timeout=10000, ) - patched = _api_client._patch_http_options(original_options, patch_options) + patched = _api_client.patch_http_options(original_options, patch_options) assert 'user-agent' in patched.headers assert 'x-goog-api-client' in patched.headers diff --git a/google/genai/tests/client/test_retries.py b/google/genai/tests/client/test_retries.py index 48531e509..ec6de7cb9 100644 --- a/google/genai/tests/client/test_retries.py +++ b/google/genai/tests/client/test_retries.py @@ -73,7 +73,7 @@ def _httpx_response(code: int): def test_retry_args_disabled(): - args = api_client._retry_args(None) + args = api_client.retry_args(None) assert set(args.keys()) == {'stop', 'reraise'} assert args['stop'].max_attempt_number == 1 @@ -82,7 +82,7 @@ def test_retry_args_disabled(): def test_retry_args_enabled_with_defaults(): # Empty options means use the default values whereas None means no retries. - args = api_client._retry_args(types.HttpRetryOptions()) + args = api_client.retry_args(types.HttpRetryOptions()) assert set(args.keys()) == {'stop', 'retry', 'wait', 'reraise'} @@ -122,7 +122,7 @@ def fn(): raise errors.APIError.raise_for_response(_httpx_response(429)) retrying = tenacity.Retrying( - **api_client._retry_args(types.HttpRetryOptions()) + **api_client.retry_args(types.HttpRetryOptions()) ) try: @@ -147,7 +147,7 @@ def test_retry_args_enabled_with_custom_values_are_not_overridden(): jitter=0.5, http_status_codes=[408, 429], ) - retry_args = api_client._retry_args(options) + retry_args = api_client.retry_args(options) assert retry_args['stop'].max_attempt_number == 10 wait = retry_args['wait'] diff --git a/google/genai/tests/conftest.py b/google/genai/tests/conftest.py index a1726fc98..25844f3e3 100644 --- a/google/genai/tests/conftest.py +++ b/google/genai/tests/conftest.py @@ -41,6 +41,12 @@ def pytest_addoption(parser): * tap: Always replay, fail if replay files do not exist. Also sets default values for the API key and replay directory. """, ) + parser.addoption( + '--private', + action='store_true', + default=False, + help='Run private tests.', + ) # Overridden via parameterized test. diff --git a/google/genai/tests/tokens/test_create.py b/google/genai/tests/tokens/test_create.py index 1cf93f048..4b7abca27 100644 --- a/google/genai/tests/tokens/test_create.py +++ b/google/genai/tests/tokens/test_create.py @@ -30,7 +30,7 @@ pytest_helper.TestTableItem( name='test_create_no_lock', parameters=types.CreateAuthTokenParameters( - # remove v1alpha1 after v1beta is available. + # remove v1alpha after v1beta is available. config={'http_options': {'api_version': 'v1alpha'}}, ), exception_if_vertex='only supported in the Gemini Developer client', @@ -38,7 +38,7 @@ pytest_helper.TestTableItem( name='test_create_global_lock', parameters=types.CreateAuthTokenParameters( - # remove v1alpha1 after v1beta is available. + # remove v1alpha after v1beta is available. config={ 'http_options': {'api_version': 'v1alpha'}, 'uses': 2, @@ -56,7 +56,7 @@ pytest_helper.TestTableItem( name='test_create_lock_non_null_fields', parameters=types.CreateAuthTokenParameters( - # remove v1alpha1 after v1beta is available. + # remove v1alpha after v1beta is available. config={ 'http_options': {'api_version': 'v1alpha'}, 'uses': 2, @@ -75,7 +75,7 @@ pytest_helper.TestTableItem( name='test_create_lock_unset_fields_as_default', parameters=types.CreateAuthTokenParameters( - # remove v1alpha1 after v1beta is available. + # remove v1alpha after v1beta is available. config={ 'http_options': {'api_version': 'v1alpha'}, 'uses': 2, @@ -94,7 +94,7 @@ pytest_helper.TestTableItem( name='test_create_lock_additional_fields', parameters=types.CreateAuthTokenParameters( - # remove v1alpha1 after v1beta is available. + # remove v1alpha after v1beta is available. config={ 'http_options': {'api_version': 'v1alpha'}, 'uses': 2, @@ -113,7 +113,7 @@ pytest_helper.TestTableItem( name='test_create_lock_with_no_params', parameters=types.CreateAuthTokenParameters( - # remove v1alpha1 after v1beta is available. + # remove v1alpha after v1beta is available. config={ 'http_options': {'api_version': 'v1alpha'}, 'lock_additional_fields': ['output_audio_transcription'], @@ -124,7 +124,7 @@ pytest_helper.TestTableItem( name='test_create_lock_with_empty_params', parameters=types.CreateAuthTokenParameters( - # remove v1alpha1 after v1beta is available. + # remove v1alpha after v1beta is available. config={ 'http_options': {'api_version': 'v1alpha'}, 'lock_additional_fields': ['output_audio_transcription'], diff --git a/google/genai/tunings.py b/google/genai/tunings.py index e0f1239f4..9883ac37d 100644 --- a/google/genai/tunings.py +++ b/google/genai/tunings.py @@ -28,6 +28,7 @@ from ._common import set_value_by_path as setv from .pagers import AsyncPager, Pager + logger = logging.getLogger('google_genai.tunings') From dd9ac87053e97d55a46fb896ce16dfbc79fe2624 Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Sat, 16 Aug 2025 09:41:28 -0700 Subject: [PATCH 5/9] chore: Refresh docs for 1.30.0 PiperOrigin-RevId: 795859299 --- docs/genai.html | 9000 +++++++++++++++++++++---------------------- docs/genindex.html | 548 ++- docs/index.html | 328 +- docs/modules.html | 328 +- docs/objects.inv | Bin 18979 -> 19675 bytes docs/searchindex.js | 2 +- 6 files changed, 5121 insertions(+), 5085 deletions(-) diff --git a/docs/genai.html b/docs/genai.html index 3908cb1df..6147992a7 100644 --- a/docs/genai.html +++ b/docs/genai.html @@ -2299,6 +2299,49 @@