Vertex AI API . projects . locations . datasets

assemble(name, body=None, x__xgafv=None)

Assembles each row of a multimodal dataset and writes the result into a BigQuery table.

Args:
  name: string, Required. The name of the Dataset resource (used only for MULTIMODAL datasets). Format: `projects/{project}/locations/{location}/datasets/{dataset}` (required)
  body: object, The request body.
    The object takes the form of:

{ # Request message for DatasetService.AssembleData. Used only for MULTIMODAL datasets.
  "geminiRequestReadConfig": { # Configuration for how to read Gemini requests from a multimodal dataset. # Optional. The read config for the dataset.
    "assembledRequestColumnName": "A String", # Optional. Column name in the dataset table that contains already fully assembled Gemini requests.
    "templateConfig": { # Template configuration to create Gemini examples from a multimodal dataset. # Gemini request template with placeholders.
      "fieldMapping": { # Required. Map of template parameters to the columns in the dataset table.
        "a_key": "A String",
      },
      "geminiExample": { # Format for Gemini examples used for Vertex Multimodal datasets. # Required. The template that will be used for assembling the request to use for downstream applications.
        "cachedContent": "A String", # Optional. The name of the cached content used as context to serve the prediction. Note: only used in explicit caching, where users can have control over caching (e.g. what content to cache) and enjoy guaranteed cost savings. Format: `projects/{project}/locations/{location}/cachedContents/{cachedContent}`
        "contents": [ # Required. The content of the current conversation with the model. For single-turn queries, this is a single instance. For multi-turn queries, this is a repeated field that contains conversation history + latest request.
          { # The base structured datatype containing multi-part content of a message. A `Content` includes a `role` field designating the producer of the `Content` and a `parts` field containing multi-part data that contains the content of the message turn.
            "parts": [ # Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types.
              { # A datatype containing media that is part of a multi-part `Content` message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. A `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
                "codeExecutionResult": { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. Result of executing the [ExecutableCode].
                  "outcome": "A String", # Required. Outcome of the code execution.
                  "output": "A String", # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
                },
                "executableCode": { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is meant to be executed.
                  "code": "A String", # Required. The code to be executed.
                  "language": "A String", # Required. Programming language of the `code`.
                },
                "fileData": { # URI based data. # Optional. URI based data.
                  "displayName": "A String", # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                  "fileUri": "A String", # Required. URI.
                  "mimeType": "A String", # Required. The IANA standard MIME type of the source data.
                },
                "functionCall": { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values.
                  "args": { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
                    "a_key": "", # Properties of the object.
                  },
                  "id": "A String", # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
                  "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name].
                },
                "functionResponse": { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # 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.
                  "id": "A String", # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
                  "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
                  "response": { # 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.
                    "a_key": "", # Properties of the object.
                  },
                },
                "inlineData": { # Content blob. # Optional. Inlined bytes data.
                  "data": "A String", # Required. Raw bytes.
                  "displayName": "A String", # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                  "mimeType": "A String", # Required. The IANA standard MIME type of the source data.
                },
                "text": "A String", # Optional. Text part (can be code).
                "thought": True or False, # Optional. Indicates if the part is thought from the model.
                "thoughtSignature": "A String", # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
                "videoMetadata": { # Metadata describes the input video content. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
                  "endOffset": "A String", # Optional. The end offset of the video.
                  "fps": 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value will be 1.0. The fps range is (0.0, 24.0].
                  "startOffset": "A String", # Optional. The start offset of the video.
                },
              },
            ],
            "role": "A String", # Optional. The producer of the content. Must be either 'user' or 'model'. Useful to set for multi-turn conversations, otherwise can be left blank or unset.
          },
        ],
        "generationConfig": { # Generation config. # Optional. Generation config.
          "audioTimestamp": True or False, # Optional. If enabled, audio timestamp will be included in the request to the model.
          "candidateCount": 42, # Optional. Number of candidates to generate.
          "enableAffectiveDialog": True or False, # Optional. If enabled, the model will detect emotions and adapt its responses accordingly.
          "frequencyPenalty": 3.14, # Optional. Frequency penalties.
          "logprobs": 42, # Optional. Logit probabilities.
          "maxOutputTokens": 42, # Optional. The maximum number of output tokens to generate per message.
          "mediaResolution": "A String", # Optional. If specified, the media resolution specified will be used.
          "modelConfig": { # Config for model selection. # Optional. Config for model selection.
            "featureSelectionPreference": "A String", # Required. Feature selection preference.
          },
          "presencePenalty": 3.14, # Optional. Positive penalties.
          "responseJsonSchema": "", # Optional. Output schema of the generated response. This is an alternative to `response_schema` that accepts [JSON Schema](https://json-schema.org/). If set, `response_schema` must be omitted, but `response_mime_type` is required. While the full JSON Schema may be sent, not all features are supported. Specifically, only the following properties are supported: - `$id` - `$defs` - `$ref` - `$anchor` - `type` - `format` - `title` - `description` - `enum` (for strings and numbers) - `items` - `prefixItems` - `minItems` - `maxItems` - `minimum` - `maximum` - `anyOf` - `oneOf` (interpreted the same as `anyOf`) - `properties` - `additionalProperties` - `required` The non-standard `propertyOrdering` property may also be set. Cyclic references are unrolled to a limited degree and, as such, may only be used within non-required properties. (Nullable properties are not sufficient.) If `$ref` is set on a sub-schema, no other properties, except for than those starting as a `$`, may be set.
          "responseLogprobs": True or False, # Optional. If true, export the logprobs results in response.
          "responseMimeType": "A String", # Optional. Output response mimetype of the generated candidate text. Supported mimetype: - `text/plain`: (default) Text output. - `application/json`: JSON response in the candidates. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined. This is a preview feature.
          "responseModalities": [ # Optional. The modalities of the response.
            "A String",
          ],
          "responseSchema": { # Schema is used to define the format of input/output data. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema-object). More fields may be added in the future as needed. # Optional. The `Schema` object allows the definition of input and output data types. These types can be objects, but also primitives and arrays. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema). If set, a compatible response_mime_type must also be set. Compatible mimetypes: `application/json`: Schema for JSON response.
            "additionalProperties": "", # Optional. Can either be a boolean or an object; controls the presence of additional properties.
            "anyOf": [ # Optional. The value should be validated against any (one or more) of the subschemas in the list.
              # Object with schema name: GoogleCloudAiplatformV1beta1Schema
            ],
            "default": "", # Optional. Default value of the data.
            "defs": { # Optional. A map of definitions for use by `ref` Only allowed at the root of the schema.
              "a_key": # Object with schema name: GoogleCloudAiplatformV1beta1Schema
            },
            "description": "A String", # Optional. The description of the data.
            "enum": [ # Optional. Possible values of the element of primitive type with enum format. Examples: 1. We can define direction as : {type:STRING, format:enum, enum:["EAST", NORTH", "SOUTH", "WEST"]} 2. We can define apartment number as : {type:INTEGER, format:enum, enum:["101", "201", "301"]}
              "A String",
            ],
            "example": "", # Optional. Example of the object. Will only populated when the object is the root.
            "format": "A String", # Optional. The format of the data. Supported formats: for NUMBER type: "float", "double" for INTEGER type: "int32", "int64" for STRING type: "email", "byte", etc
            "items": # Object with schema name: GoogleCloudAiplatformV1beta1Schema # Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY.
            "maxItems": "A String", # Optional. Maximum number of the elements for Type.ARRAY.
            "maxLength": "A String", # Optional. Maximum length of the Type.STRING
            "maxProperties": "A String", # Optional. Maximum number of the properties for Type.OBJECT.
            "maximum": 3.14, # Optional. Maximum value of the Type.INTEGER and Type.NUMBER
            "minItems": "A String", # Optional. Minimum number of the elements for Type.ARRAY.
            "minLength": "A String", # Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING
            "minProperties": "A String", # Optional. Minimum number of the properties for Type.OBJECT.
            "minimum": 3.14, # Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER
            "nullable": True or False, # Optional. Indicates if the value may be null.
            "pattern": "A String", # Optional. Pattern of the Type.STRING to restrict a string to a regular expression.
            "properties": { # Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT.
              "a_key": # Object with schema name: GoogleCloudAiplatformV1beta1Schema
            },
            "propertyOrdering": [ # Optional. The order of the properties. Not a standard field in open api spec. Only used to support the order of the properties.
              "A String",
            ],
            "ref": "A String", # Optional. Allows indirect references between schema nodes. The value should be a valid reference to a child of the root `defs`. For example, the following schema defines a reference to a schema node named "Pet": type: object properties: pet: ref: #/defs/Pet defs: Pet: type: object properties: name: type: string The value of the "pet" property is a reference to the schema node named "Pet". See details in https://json-schema.org/understanding-json-schema/structuring
            "required": [ # Optional. Required properties of Type.OBJECT.
              "A String",
            ],
            "title": "A String", # Optional. The title of the Schema.
            "type": "A String", # Optional. The type of the data.
          },
          "routingConfig": { # The configuration for routing the request to a specific model. # Optional. Routing configuration.
            "autoMode": { # When automated routing is specified, the routing will be determined by the pretrained routing model and customer provided model routing preference. # Automated routing.
              "modelRoutingPreference": "A String", # The model routing preference.
            },
            "manualMode": { # When manual routing is set, the specified model will be used directly. # Manual routing.
              "modelName": "A String", # The model name to use. Only the public LLM models are accepted. See [Supported models](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference#supported-models).
            },
          },
          "seed": 42, # Optional. Seed.
          "speechConfig": { # The speech generation config. # Optional. The speech generation config.
            "languageCode": "A String", # Optional. Language code (ISO 639. e.g. en-US) for the speech synthesization.
            "voiceConfig": { # The configuration for the voice to use. # The configuration for the speaker to use.
              "prebuiltVoiceConfig": { # The configuration for the prebuilt speaker to use. # The configuration for the prebuilt voice to use.
                "voiceName": "A String", # The name of the preset voice to use.
              },
            },
          },
          "stopSequences": [ # Optional. Stop sequences.
            "A String",
          ],
          "temperature": 3.14, # Optional. Controls the randomness of predictions.
          "thinkingConfig": { # Config for thinking features. # Optional. Config for thinking features. An error will be returned if this field is set for models that don't support thinking.
            "includeThoughts": True or False, # Optional. Indicates whether to include thoughts in the response. If true, thoughts are returned only when available.
            "thinkingBudget": 42, # Optional. Indicates the thinking budget in tokens.
          },
          "topK": 3.14, # Optional. If specified, top-k sampling will be used.
          "topP": 3.14, # Optional. If specified, nucleus sampling will be used.
        },
        "labels": { # Optional. The labels with user-defined metadata for the request. It is used for billing and reporting only. Label keys and values can be no longer than 63 characters (Unicode codepoints) and can only contain lowercase letters, numeric characters, underscores, and dashes. International characters are allowed. Label values are optional. Label keys must start with a letter.
          "a_key": "A String",
        },
        "model": "A String", # Optional. The fully qualified name of the publisher model or tuned model endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Tuned model endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
        "safetySettings": [ # Optional. Per request settings for blocking unsafe content. Enforced on GenerateContentResponse.candidates.
          { # Safety settings.
            "category": "A String", # Required. Harm category.
            "method": "A String", # Optional. Specify if the threshold is used for probability or severity score. If not specified, the threshold is used for probability score.
            "threshold": "A String", # Required. The harm block threshold.
          },
        ],
        "systemInstruction": { # The base structured datatype containing multi-part content of a message. A `Content` includes a `role` field designating the producer of the `Content` and a `parts` field containing multi-part data that contains the content of the message turn. # Optional. The user provided system instructions for the model. Note: only text should be used in parts and content in each part will be in a separate paragraph.
          "parts": [ # Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types.
            { # A datatype containing media that is part of a multi-part `Content` message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. A `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
              "codeExecutionResult": { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. Result of executing the [ExecutableCode].
                "outcome": "A String", # Required. Outcome of the code execution.
                "output": "A String", # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
              },
              "executableCode": { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is meant to be executed.
                "code": "A String", # Required. The code to be executed.
                "language": "A String", # Required. Programming language of the `code`.
              },
              "fileData": { # URI based data. # Optional. URI based data.
                "displayName": "A String", # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                "fileUri": "A String", # Required. URI.
                "mimeType": "A String", # Required. The IANA standard MIME type of the source data.
              },
              "functionCall": { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values.
                "args": { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
                  "a_key": "", # Properties of the object.
                },
                "id": "A String", # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
                "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name].
              },
              "functionResponse": { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # 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.
                "id": "A String", # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
                "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
                "response": { # 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.
                  "a_key": "", # Properties of the object.
                },
              },
              "inlineData": { # Content blob. # Optional. Inlined bytes data.
                "data": "A String", # Required. Raw bytes.
                "displayName": "A String", # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                "mimeType": "A String", # Required. The IANA standard MIME type of the source data.
              },
              "text": "A String", # Optional. Text part (can be code).
              "thought": True or False, # Optional. Indicates if the part is thought from the model.
              "thoughtSignature": "A String", # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
              "videoMetadata": { # Metadata describes the input video content. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
                "endOffset": "A String", # Optional. The end offset of the video.
                "fps": 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value will be 1.0. The fps range is (0.0, 24.0].
                "startOffset": "A String", # Optional. The start offset of the video.
              },
            },
          ],
          "role": "A String", # Optional. The producer of the content. Must be either 'user' or 'model'. Useful to set for multi-turn conversations, otherwise can be left blank or unset.
        },
        "toolConfig": { # Tool config. This config is shared for all tools provided in the request. # Optional. Tool config. This config is shared for all tools provided in the request.
          "functionCallingConfig": { # Function calling config. # Optional. Function calling config.
            "allowedFunctionNames": [ # Optional. Function names to call. Only set when the Mode is ANY. Function names should match [FunctionDeclaration.name]. With mode set to ANY, model will predict a function call from the set of function names provided.
              "A String",
            ],
            "mode": "A String", # Optional. Function calling mode.
          },
          "retrievalConfig": { # Retrieval config. # Optional. Retrieval config.
            "languageCode": "A String", # The language code of the user.
            "latLng": { # An object that represents a latitude/longitude pair. This is expressed as a pair of doubles to represent degrees latitude and degrees longitude. Unless specified otherwise, this object must conform to the WGS84 standard. Values must be within normalized ranges. # The location of the user.
              "latitude": 3.14, # The latitude in degrees. It must be in the range [-90.0, +90.0].
              "longitude": 3.14, # The longitude in degrees. It must be in the range [-180.0, +180.0].
            },
          },
        },
        "tools": [ # Optional. A list of `Tools` the model may use to generate the next response. A `Tool` is a piece of code that enables the system to interact with external systems to perform an action, or set of actions, outside of knowledge and scope of the model.
          { # Tool details that the model may use to generate response. A `Tool` is a piece of code that enables the system to interact with external systems to perform an action, or set of actions, outside of knowledge and scope of the model. A Tool object should contain exactly one type of Tool (e.g FunctionDeclaration, Retrieval or GoogleSearchRetrieval).
            "codeExecution": { # Tool that executes code generated by the model, and automatically returns the result to the model. See also [ExecutableCode]and [CodeExecutionResult] which are input and output to this tool. # Optional. CodeExecution tool type. Enables the model to execute code as part of generation.
            },
            "enterpriseWebSearch": { # Tool to search public web data, powered by Vertex AI Search and Sec4 compliance. # Optional. Tool to support searching public web data, powered by Vertex AI Search and Sec4 compliance.
              "excludeDomains": [ # Optional. List of domains to be excluded from the search results. The default limit is 2000 domains.
                "A String",
              ],
            },
            "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
              { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
                "description": "A String", # Optional. Description and purpose of the function. Model uses it to decide how and whether to call the function.
                "name": "A String", # Required. The name of the function to call. Must start with a letter or an underscore. Must be a-z, A-Z, 0-9, or contain underscores, dots and dashes, with a maximum length of 64.
                "parameters": { # Schema is used to define the format of input/output data. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema-object). More fields may be added in the future as needed. # Optional. Describes the parameters to this function in JSON Schema Object format. Reflects the Open API 3.03 Parameter Object. string Key: the name of the parameter. Parameter names are case sensitive. Schema Value: the Schema defining the type used for the parameter. For function with no parameters, this can be left unset. Parameter names must start with a letter or an underscore and must only contain chars a-z, A-Z, 0-9, or underscores with a maximum length of 64. Example with 1 required and 1 optional parameter: type: OBJECT properties: param1: type: STRING param2: type: INTEGER required: - param1
                  "additionalProperties": "", # Optional. Can either be a boolean or an object; controls the presence of additional properties.
                  "anyOf": [ # Optional. The value should be validated against any (one or more) of the subschemas in the list.
                    # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                  ],
                  "default": "", # Optional. Default value of the data.
                  "defs": { # Optional. A map of definitions for use by `ref` Only allowed at the root of the schema.
                    "a_key": # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                  },
                  "description": "A String", # Optional. The description of the data.
                  "enum": [ # Optional. Possible values of the element of primitive type with enum format. Examples: 1. We can define direction as : {type:STRING, format:enum, enum:["EAST", NORTH", "SOUTH", "WEST"]} 2. We can define apartment number as : {type:INTEGER, format:enum, enum:["101", "201", "301"]}
                    "A String",
                  ],
                  "example": "", # Optional. Example of the object. Will only populated when the object is the root.
                  "format": "A String", # Optional. The format of the data. Supported formats: for NUMBER type: "float", "double" for INTEGER type: "int32", "int64" for STRING type: "email", "byte", etc
                  "items": # Object with schema name: GoogleCloudAiplatformV1beta1Schema # Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY.
                  "maxItems": "A String", # Optional. Maximum number of the elements for Type.ARRAY.
                  "maxLength": "A String", # Optional. Maximum length of the Type.STRING
                  "maxProperties": "A String", # Optional. Maximum number of the properties for Type.OBJECT.
                  "maximum": 3.14, # Optional. Maximum value of the Type.INTEGER and Type.NUMBER
                  "minItems": "A String", # Optional. Minimum number of the elements for Type.ARRAY.
                  "minLength": "A String", # Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING
                  "minProperties": "A String", # Optional. Minimum number of the properties for Type.OBJECT.
                  "minimum": 3.14, # Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER
                  "nullable": True or False, # Optional. Indicates if the value may be null.
                  "pattern": "A String", # Optional. Pattern of the Type.STRING to restrict a string to a regular expression.
                  "properties": { # Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT.
                    "a_key": # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                  },
                  "propertyOrdering": [ # Optional. The order of the properties. Not a standard field in open api spec. Only used to support the order of the properties.
                    "A String",
                  ],
                  "ref": "A String", # Optional. Allows indirect references between schema nodes. The value should be a valid reference to a child of the root `defs`. For example, the following schema defines a reference to a schema node named "Pet": type: object properties: pet: ref: #/defs/Pet defs: Pet: type: object properties: name: type: string The value of the "pet" property is a reference to the schema node named "Pet". See details in https://json-schema.org/understanding-json-schema/structuring
                  "required": [ # Optional. Required properties of Type.OBJECT.
                    "A String",
                  ],
                  "title": "A String", # Optional. The title of the Schema.
                  "type": "A String", # Optional. The type of the data.
                },
                "parametersJsonSchema": "", # Optional. Describes the parameters to the function in JSON Schema format. The schema must describe an object where the properties are the parameters to the function. For example: ``` { "type": "object", "properties": { "name": { "type": "string" }, "age": { "type": "integer" } }, "additionalProperties": false, "required": ["name", "age"], "propertyOrdering": ["name", "age"] } ``` This field is mutually exclusive with `parameters`.
                "response": { # Schema is used to define the format of input/output data. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema-object). More fields may be added in the future as needed. # Optional. Describes the output from this function in JSON Schema format. Reflects the Open API 3.03 Response Object. The Schema defines the type used for the response value of the function.
                  "additionalProperties": "", # Optional. Can either be a boolean or an object; controls the presence of additional properties.
                  "anyOf": [ # Optional. The value should be validated against any (one or more) of the subschemas in the list.
                    # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                  ],
                  "default": "", # Optional. Default value of the data.
                  "defs": { # Optional. A map of definitions for use by `ref` Only allowed at the root of the schema.
                    "a_key": # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                  },
                  "description": "A String", # Optional. The description of the data.
                  "enum": [ # Optional. Possible values of the element of primitive type with enum format. Examples: 1. We can define direction as : {type:STRING, format:enum, enum:["EAST", NORTH", "SOUTH", "WEST"]} 2. We can define apartment number as : {type:INTEGER, format:enum, enum:["101", "201", "301"]}
                    "A String",
                  ],
                  "example": "", # Optional. Example of the object. Will only populated when the object is the root.
                  "format": "A String", # Optional. The format of the data. Supported formats: for NUMBER type: "float", "double" for INTEGER type: "int32", "int64" for STRING type: "email", "byte", etc
                  "items": # Object with schema name: GoogleCloudAiplatformV1beta1Schema # Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY.
                  "maxItems": "A String", # Optional. Maximum number of the elements for Type.ARRAY.
                  "maxLength": "A String", # Optional. Maximum length of the Type.STRING
                  "maxProperties": "A String", # Optional. Maximum number of the properties for Type.OBJECT.
                  "maximum": 3.14, # Optional. Maximum value of the Type.INTEGER and Type.NUMBER
                  "minItems": "A String", # Optional. Minimum number of the elements for Type.ARRAY.
                  "minLength": "A String", # Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING
                  "minProperties": "A String", # Optional. Minimum number of the properties for Type.OBJECT.
                  "minimum": 3.14, # Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER
                  "nullable": True or False, # Optional. Indicates if the value may be null.
                  "pattern": "A String", # Optional. Pattern of the Type.STRING to restrict a string to a regular expression.
                  "properties": { # Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT.
                    "a_key": # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                  },
                  "propertyOrdering": [ # Optional. The order of the properties. Not a standard field in open api spec. Only used to support the order of the properties.
                    "A String",
                  ],
                  "ref": "A String", # Optional. Allows indirect references between schema nodes. The value should be a valid reference to a child of the root `defs`. For example, the following schema defines a reference to a schema node named "Pet": type: object properties: pet: ref: #/defs/Pet defs: Pet: type: object properties: name: type: string The value of the "pet" property is a reference to the schema node named "Pet". See details in https://json-schema.org/understanding-json-schema/structuring
                  "required": [ # Optional. Required properties of Type.OBJECT.
                    "A String",
                  ],
                  "title": "A String", # Optional. The title of the Schema.
                  "type": "A String", # Optional. The type of the data.
                },
                "responseJsonSchema": "", # Optional. Describes the output from this function in JSON Schema format. The value specified by the schema is the response value of the function. This field is mutually exclusive with `response`.
              },
            ],
            "googleMaps": { # Tool to retrieve public maps data for grounding, powered by Google. # Optional. GoogleMaps tool type. Tool to support Google Maps in Model.
              "enableWidget": True or False, # Optional. If true, include the widget context token in the response.
            },
            "googleSearch": { # GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. # Optional. GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google.
              "excludeDomains": [ # Optional. List of domains to be excluded from the search results. The default limit is 2000 domains. Example: ["amazon.com", "facebook.com"].
                "A String",
              ],
            },
            "googleSearchRetrieval": { # Tool to retrieve public web data for grounding, powered by Google. # Optional. Specialized retrieval tool that is powered by Google Search.
              "dynamicRetrievalConfig": { # Describes the options to customize dynamic retrieval. # Specifies the dynamic retrieval configuration for the given source.
                "dynamicThreshold": 3.14, # Optional. The threshold to be used in dynamic retrieval. If not set, a system default value is used.
                "mode": "A String", # The mode of the predictor to be used in dynamic retrieval.
              },
            },
            "retrieval": { # Defines a retrieval tool that model can call to access external knowledge. # Optional. Retrieval tool type. System will always execute the provided retrieval tool(s) to get external knowledge to answer the prompt. Retrieval results are presented to the model for generation.
              "disableAttribution": True or False, # Optional. Deprecated. This option is no longer supported.
              "externalApi": { # Retrieve from data source powered by external API for grounding. The external API is not owned by Google, but need to follow the pre-defined API spec. # Use data source powered by external API for grounding.
                "apiAuth": { # The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. # The authentication config to access the API. Deprecated. Please use auth_config instead.
                  "apiKeyConfig": { # The API secret. # The API secret.
                    "apiKeySecretVersion": "A String", # Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version}
                    "apiKeyString": "A String", # The API key string. Either this or `api_key_secret_version` must be set.
                  },
                },
                "apiSpec": "A String", # The API spec that the external API implements.
                "authConfig": { # Auth configuration to run the extension. # The authentication config to access the API.
                  "apiKeyConfig": { # Config for authentication with API key. # Config for API key auth.
                    "apiKeySecret": "A String", # Optional. The name of the SecretManager secret version resource storing the API key. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If both `api_key_secret` and `api_key_string` are specified, this field takes precedence over `api_key_string`. - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
                    "apiKeyString": "A String", # Optional. The API key to be used in the request directly.
                    "httpElementLocation": "A String", # Optional. The location of the API key.
                    "name": "A String", # Optional. The parameter name of the API key. E.g. If the API request is "https://example.com/act?api_key=", "api_key" would be the parameter name.
                  },
                  "authType": "A String", # Type of auth scheme.
                  "googleServiceAccountConfig": { # Config for Google Service Account Authentication. # Config for Google Service Account auth.
                    "serviceAccount": "A String", # Optional. The service account that the extension execution service runs as. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. - If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension.
                  },
                  "httpBasicAuthConfig": { # Config for HTTP Basic Authentication. # Config for HTTP Basic auth.
                    "credentialSecret": "A String", # Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
                  },
                  "oauthConfig": { # Config for user oauth. # Config for user oauth.
                    "accessToken": "A String", # Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
                    "serviceAccount": "A String", # The service account used to generate access tokens for executing the Extension. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account.
                  },
                  "oidcConfig": { # Config for user OIDC auth. # Config for user OIDC auth.
                    "idToken": "A String", # OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
                    "serviceAccount": "A String", # The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc). - The audience for the token will be set to the URL in the server url defined in the OpenApi spec. - If the service account is provided, the service account should grant `iam.serviceAccounts.getOpenIdToken` permission to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents).
                  },
                },
                "elasticSearchParams": { # The search parameters to use for the ELASTIC_SEARCH spec. # Parameters for the elastic search API.
                  "index": "A String", # The ElasticSearch index to use.
                  "numHits": 42, # Optional. Number of hits (chunks) to request. When specified, it is passed to Elasticsearch as the `num_hits` param.
                  "searchTemplate": "A String", # The ElasticSearch search template to use.
                },
                "endpoint": "A String", # The endpoint of the external API. The system will call the API at this endpoint to retrieve the data for grounding. Example: https://acme.com:443/search
                "simpleSearchParams": { # The search parameters to use for SIMPLE_SEARCH spec. # Parameters for the simple search API.
                },
              },
              "vertexAiSearch": { # Retrieve from Vertex AI Search datastore or engine for grounding. datastore and engine are mutually exclusive. See https://cloud.google.com/products/agent-builder # Set to use data source powered by Vertex AI Search.
                "dataStoreSpecs": [ # Specifications that define the specific DataStores to be searched, along with configurations for those data stores. This is only considered for Engines with multiple data stores. It should only be set if engine is used.
                  { # Define data stores within engine to filter on in a search call and configurations for those data stores. For more information, see https://cloud.google.com/generative-ai-app-builder/docs/reference/rpc/google.cloud.discoveryengine.v1#datastorespec
                    "dataStore": "A String", # Full resource name of DataStore, such as Format: `projects/{project}/locations/{location}/collections/{collection}/dataStores/{dataStore}`
                    "filter": "A String", # Optional. Filter specification to filter documents in the data store specified by data_store field. For more information on filtering, see [Filtering](https://cloud.google.com/generative-ai-app-builder/docs/filter-search-metadata)
                  },
                ],
                "datastore": "A String", # Optional. Fully-qualified Vertex AI Search data store resource ID. Format: `projects/{project}/locations/{location}/collections/{collection}/dataStores/{dataStore}`
                "engine": "A String", # Optional. Fully-qualified Vertex AI Search engine resource ID. Format: `projects/{project}/locations/{location}/collections/{collection}/engines/{engine}`
                "filter": "A String", # Optional. Filter strings to be passed to the search API.
                "maxResults": 42, # Optional. Number of search results to return per query. The default value is 10. The maximumm allowed value is 10.
              },
              "vertexRagStore": { # Retrieve from Vertex RAG Store for grounding. # Set to use data source powered by Vertex RAG store. User data is uploaded via the VertexRagDataService.
                "ragCorpora": [ # Optional. Deprecated. Please use rag_resources instead.
                  "A String",
                ],
                "ragResources": [ # Optional. The representation of the rag source. It can be used to specify corpus only or ragfiles. Currently only support one corpus or multiple files from one corpus. In the future we may open up multiple corpora support.
                  { # The definition of the Rag resource.
                    "ragCorpus": "A String", # Optional. RagCorpora resource name. Format: `projects/{project}/locations/{location}/ragCorpora/{rag_corpus}`
                    "ragFileIds": [ # Optional. rag_file_id. The files should be in the same rag_corpus set in rag_corpus field.
                      "A String",
                    ],
                  },
                ],
                "ragRetrievalConfig": { # Specifies the context retrieval config. # Optional. The retrieval config for the Rag query.
                  "filter": { # Config for filters. # Optional. Config for filters.
                    "metadataFilter": "A String", # Optional. String for metadata filtering.
                    "vectorDistanceThreshold": 3.14, # Optional. Only returns contexts with vector distance smaller than the threshold.
                    "vectorSimilarityThreshold": 3.14, # Optional. Only returns contexts with vector similarity larger than the threshold.
                  },
                  "hybridSearch": { # Config for Hybrid Search. # Optional. Config for Hybrid Search.
                    "alpha": 3.14, # Optional. Alpha value controls the weight between dense and sparse vector search results. The range is [0, 1], while 0 means sparse vector search only and 1 means dense vector search only. The default value is 0.5 which balances sparse and dense vector search equally.
                  },
                  "ranking": { # Config for ranking and reranking. # Optional. Config for ranking and reranking.
                    "llmRanker": { # Config for LlmRanker. # Optional. Config for LlmRanker.
                      "modelName": "A String", # Optional. The model name used for ranking. See [Supported models](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference#supported-models).
                    },
                    "rankService": { # Config for Rank Service. # Optional. Config for Rank Service.
                      "modelName": "A String", # Optional. The model name of the rank service. Format: `semantic-ranker-512@latest`
                    },
                  },
                  "topK": 42, # Optional. The number of contexts to retrieve.
                },
                "similarityTopK": 42, # Optional. Number of top k results to return from the selected corpora.
                "storeContext": True or False, # Optional. Currently only supported for Gemini Multimodal Live API. In Gemini Multimodal Live API, if `store_context` bool is specified, Gemini will leverage it to automatically memorize the interactions between the client and Gemini, and retrieve context when needed to augment the response generation for users' ongoing and future interactions.
                "vectorDistanceThreshold": 3.14, # Optional. Only return results with vector distance smaller than the threshold.
              },
            },
            "urlContext": { # Tool to support URL context. # Optional. Tool to support URL context retrieval.
            },
          },
        ],
      },
    },
  },
}

  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # This resource represents a long-running operation that is the result of a network API call.
  "done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
  "error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
    "code": 42, # The status code, which should be an enum value of google.rpc.Code.
    "details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
      {
        "a_key": "", # Properties of the object. Contains field @type with type URL.
      },
    ],
    "message": "A String", # 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.
  },
  "metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
    "a_key": "", # Properties of the object. Contains field @type with type URL.
  },
  "name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
  "response": { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
    "a_key": "", # Properties of the object. Contains field @type with type URL.
  },
}

assess(name, body=None, x__xgafv=None)

Assesses the state or validity of the dataset with respect to a given use case.

Args:
  name: string, Required. The name of the Dataset resource. Used only for MULTIMODAL datasets. Format: `projects/{project}/locations/{location}/datasets/{dataset}` (required)
  body: object, The request body.
    The object takes the form of:

{ # Request message for DatasetService.AssessData. Used only for MULTIMODAL datasets.
  "batchPredictionResourceUsageAssessmentConfig": { # Configuration for the batch prediction resource usage assessment. # Optional. Configuration for the batch prediction resource usage assessment.
    "modelName": "A String", # Required. The name of the model used for batch prediction.
  },
  "batchPredictionValidationAssessmentConfig": { # Configuration for the batch prediction validation assessment. # Optional. Configuration for the batch prediction validation assessment.
    "modelName": "A String", # Required. The name of the model used for batch prediction.
  },
  "geminiRequestReadConfig": { # Configuration for how to read Gemini requests from a multimodal dataset. # Optional. The Gemini request read config for the dataset.
    "assembledRequestColumnName": "A String", # Optional. Column name in the dataset table that contains already fully assembled Gemini requests.
    "templateConfig": { # Template configuration to create Gemini examples from a multimodal dataset. # Gemini request template with placeholders.
      "fieldMapping": { # Required. Map of template parameters to the columns in the dataset table.
        "a_key": "A String",
      },
      "geminiExample": { # Format for Gemini examples used for Vertex Multimodal datasets. # Required. The template that will be used for assembling the request to use for downstream applications.
        "cachedContent": "A String", # Optional. The name of the cached content used as context to serve the prediction. Note: only used in explicit caching, where users can have control over caching (e.g. what content to cache) and enjoy guaranteed cost savings. Format: `projects/{project}/locations/{location}/cachedContents/{cachedContent}`
        "contents": [ # Required. The content of the current conversation with the model. For single-turn queries, this is a single instance. For multi-turn queries, this is a repeated field that contains conversation history + latest request.
          { # The base structured datatype containing multi-part content of a message. A `Content` includes a `role` field designating the producer of the `Content` and a `parts` field containing multi-part data that contains the content of the message turn.
            "parts": [ # Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types.
              { # A datatype containing media that is part of a multi-part `Content` message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. A `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
                "codeExecutionResult": { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. Result of executing the [ExecutableCode].
                  "outcome": "A String", # Required. Outcome of the code execution.
                  "output": "A String", # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
                },
                "executableCode": { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is meant to be executed.
                  "code": "A String", # Required. The code to be executed.
                  "language": "A String", # Required. Programming language of the `code`.
                },
                "fileData": { # URI based data. # Optional. URI based data.
                  "displayName": "A String", # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                  "fileUri": "A String", # Required. URI.
                  "mimeType": "A String", # Required. The IANA standard MIME type of the source data.
                },
                "functionCall": { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values.
                  "args": { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
                    "a_key": "", # Properties of the object.
                  },
                  "id": "A String", # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
                  "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name].
                },
                "functionResponse": { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # 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.
                  "id": "A String", # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
                  "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
                  "response": { # 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.
                    "a_key": "", # Properties of the object.
                  },
                },
                "inlineData": { # Content blob. # Optional. Inlined bytes data.
                  "data": "A String", # Required. Raw bytes.
                  "displayName": "A String", # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                  "mimeType": "A String", # Required. The IANA standard MIME type of the source data.
                },
                "text": "A String", # Optional. Text part (can be code).
                "thought": True or False, # Optional. Indicates if the part is thought from the model.
                "thoughtSignature": "A String", # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
                "videoMetadata": { # Metadata describes the input video content. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
                  "endOffset": "A String", # Optional. The end offset of the video.
                  "fps": 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value will be 1.0. The fps range is (0.0, 24.0].
                  "startOffset": "A String", # Optional. The start offset of the video.
                },
              },
            ],
            "role": "A String", # Optional. The producer of the content. Must be either 'user' or 'model'. Useful to set for multi-turn conversations, otherwise can be left blank or unset.
          },
        ],
        "generationConfig": { # Generation config. # Optional. Generation config.
          "audioTimestamp": True or False, # Optional. If enabled, audio timestamp will be included in the request to the model.
          "candidateCount": 42, # Optional. Number of candidates to generate.
          "enableAffectiveDialog": True or False, # Optional. If enabled, the model will detect emotions and adapt its responses accordingly.
          "frequencyPenalty": 3.14, # Optional. Frequency penalties.
          "logprobs": 42, # Optional. Logit probabilities.
          "maxOutputTokens": 42, # Optional. The maximum number of output tokens to generate per message.
          "mediaResolution": "A String", # Optional. If specified, the media resolution specified will be used.
          "modelConfig": { # Config for model selection. # Optional. Config for model selection.
            "featureSelectionPreference": "A String", # Required. Feature selection preference.
          },
          "presencePenalty": 3.14, # Optional. Positive penalties.
          "responseJsonSchema": "", # Optional. Output schema of the generated response. This is an alternative to `response_schema` that accepts [JSON Schema](https://json-schema.org/). If set, `response_schema` must be omitted, but `response_mime_type` is required. While the full JSON Schema may be sent, not all features are supported. Specifically, only the following properties are supported: - `$id` - `$defs` - `$ref` - `$anchor` - `type` - `format` - `title` - `description` - `enum` (for strings and numbers) - `items` - `prefixItems` - `minItems` - `maxItems` - `minimum` - `maximum` - `anyOf` - `oneOf` (interpreted the same as `anyOf`) - `properties` - `additionalProperties` - `required` The non-standard `propertyOrdering` property may also be set. Cyclic references are unrolled to a limited degree and, as such, may only be used within non-required properties. (Nullable properties are not sufficient.) If `$ref` is set on a sub-schema, no other properties, except for than those starting as a `$`, may be set.
          "responseLogprobs": True or False, # Optional. If true, export the logprobs results in response.
          "responseMimeType": "A String", # Optional. Output response mimetype of the generated candidate text. Supported mimetype: - `text/plain`: (default) Text output. - `application/json`: JSON response in the candidates. The model needs to be prompted to output the appropriate response type, otherwise the behavior is undefined. This is a preview feature.
          "responseModalities": [ # Optional. The modalities of the response.
            "A String",
          ],
          "responseSchema": { # Schema is used to define the format of input/output data. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema-object). More fields may be added in the future as needed. # Optional. The `Schema` object allows the definition of input and output data types. These types can be objects, but also primitives and arrays. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema). If set, a compatible response_mime_type must also be set. Compatible mimetypes: `application/json`: Schema for JSON response.
            "additionalProperties": "", # Optional. Can either be a boolean or an object; controls the presence of additional properties.
            "anyOf": [ # Optional. The value should be validated against any (one or more) of the subschemas in the list.
              # Object with schema name: GoogleCloudAiplatformV1beta1Schema
            ],
            "default": "", # Optional. Default value of the data.
            "defs": { # Optional. A map of definitions for use by `ref` Only allowed at the root of the schema.
              "a_key": # Object with schema name: GoogleCloudAiplatformV1beta1Schema
            },
            "description": "A String", # Optional. The description of the data.
            "enum": [ # Optional. Possible values of the element of primitive type with enum format. Examples: 1. We can define direction as : {type:STRING, format:enum, enum:["EAST", NORTH", "SOUTH", "WEST"]} 2. We can define apartment number as : {type:INTEGER, format:enum, enum:["101", "201", "301"]}
              "A String",
            ],
            "example": "", # Optional. Example of the object. Will only populated when the object is the root.
            "format": "A String", # Optional. The format of the data. Supported formats: for NUMBER type: "float", "double" for INTEGER type: "int32", "int64" for STRING type: "email", "byte", etc
            "items": # Object with schema name: GoogleCloudAiplatformV1beta1Schema # Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY.
            "maxItems": "A String", # Optional. Maximum number of the elements for Type.ARRAY.
            "maxLength": "A String", # Optional. Maximum length of the Type.STRING
            "maxProperties": "A String", # Optional. Maximum number of the properties for Type.OBJECT.
            "maximum": 3.14, # Optional. Maximum value of the Type.INTEGER and Type.NUMBER
            "minItems": "A String", # Optional. Minimum number of the elements for Type.ARRAY.
            "minLength": "A String", # Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING
            "minProperties": "A String", # Optional. Minimum number of the properties for Type.OBJECT.
            "minimum": 3.14, # Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER
            "nullable": True or False, # Optional. Indicates if the value may be null.
            "pattern": "A String", # Optional. Pattern of the Type.STRING to restrict a string to a regular expression.
            "properties": { # Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT.
              "a_key": # Object with schema name: GoogleCloudAiplatformV1beta1Schema
            },
            "propertyOrdering": [ # Optional. The order of the properties. Not a standard field in open api spec. Only used to support the order of the properties.
              "A String",
            ],
            "ref": "A String", # Optional. Allows indirect references between schema nodes. The value should be a valid reference to a child of the root `defs`. For example, the following schema defines a reference to a schema node named "Pet": type: object properties: pet: ref: #/defs/Pet defs: Pet: type: object properties: name: type: string The value of the "pet" property is a reference to the schema node named "Pet". See details in https://json-schema.org/understanding-json-schema/structuring
            "required": [ # Optional. Required properties of Type.OBJECT.
              "A String",
            ],
            "title": "A String", # Optional. The title of the Schema.
            "type": "A String", # Optional. The type of the data.
          },
          "routingConfig": { # The configuration for routing the request to a specific model. # Optional. Routing configuration.
            "autoMode": { # When automated routing is specified, the routing will be determined by the pretrained routing model and customer provided model routing preference. # Automated routing.
              "modelRoutingPreference": "A String", # The model routing preference.
            },
            "manualMode": { # When manual routing is set, the specified model will be used directly. # Manual routing.
              "modelName": "A String", # The model name to use. Only the public LLM models are accepted. See [Supported models](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference#supported-models).
            },
          },
          "seed": 42, # Optional. Seed.
          "speechConfig": { # The speech generation config. # Optional. The speech generation config.
            "languageCode": "A String", # Optional. Language code (ISO 639. e.g. en-US) for the speech synthesization.
            "voiceConfig": { # The configuration for the voice to use. # The configuration for the speaker to use.
              "prebuiltVoiceConfig": { # The configuration for the prebuilt speaker to use. # The configuration for the prebuilt voice to use.
                "voiceName": "A String", # The name of the preset voice to use.
              },
            },
          },
          "stopSequences": [ # Optional. Stop sequences.
            "A String",
          ],
          "temperature": 3.14, # Optional. Controls the randomness of predictions.
          "thinkingConfig": { # Config for thinking features. # Optional. Config for thinking features. An error will be returned if this field is set for models that don't support thinking.
            "includeThoughts": True or False, # Optional. Indicates whether to include thoughts in the response. If true, thoughts are returned only when available.
            "thinkingBudget": 42, # Optional. Indicates the thinking budget in tokens.
          },
          "topK": 3.14, # Optional. If specified, top-k sampling will be used.
          "topP": 3.14, # Optional. If specified, nucleus sampling will be used.
        },
        "labels": { # Optional. The labels with user-defined metadata for the request. It is used for billing and reporting only. Label keys and values can be no longer than 63 characters (Unicode codepoints) and can only contain lowercase letters, numeric characters, underscores, and dashes. International characters are allowed. Label values are optional. Label keys must start with a letter.
          "a_key": "A String",
        },
        "model": "A String", # Optional. The fully qualified name of the publisher model or tuned model endpoint to use. Publisher model format: `projects/{project}/locations/{location}/publishers/*/models/*` Tuned model endpoint format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
        "safetySettings": [ # Optional. Per request settings for blocking unsafe content. Enforced on GenerateContentResponse.candidates.
          { # Safety settings.
            "category": "A String", # Required. Harm category.
            "method": "A String", # Optional. Specify if the threshold is used for probability or severity score. If not specified, the threshold is used for probability score.
            "threshold": "A String", # Required. The harm block threshold.
          },
        ],
        "systemInstruction": { # The base structured datatype containing multi-part content of a message. A `Content` includes a `role` field designating the producer of the `Content` and a `parts` field containing multi-part data that contains the content of the message turn. # Optional. The user provided system instructions for the model. Note: only text should be used in parts and content in each part will be in a separate paragraph.
          "parts": [ # Required. Ordered `Parts` that constitute a single message. Parts may have different IANA MIME types.
            { # A datatype containing media that is part of a multi-part `Content` message. A `Part` consists of data which has an associated datatype. A `Part` can only contain one of the accepted types in `Part.data`. A `Part` must have a fixed IANA MIME type identifying the type and subtype of the media if `inline_data` or `file_data` field is filled with raw bytes.
              "codeExecutionResult": { # Result of executing the [ExecutableCode]. Only generated when using the [CodeExecution] tool, and always follows a `part` containing the [ExecutableCode]. # Optional. Result of executing the [ExecutableCode].
                "outcome": "A String", # Required. Outcome of the code execution.
                "output": "A String", # Optional. Contains stdout when code execution is successful, stderr or other description otherwise.
              },
              "executableCode": { # Code generated by the model that is meant to be executed, and the result returned to the model. Generated when using the [CodeExecution] tool, in which the code will be automatically executed, and a corresponding [CodeExecutionResult] will also be generated. # Optional. Code generated by the model that is meant to be executed.
                "code": "A String", # Required. The code to be executed.
                "language": "A String", # Required. Programming language of the `code`.
              },
              "fileData": { # URI based data. # Optional. URI based data.
                "displayName": "A String", # Optional. Display name of the file data. Used to provide a label or filename to distinguish file datas. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                "fileUri": "A String", # Required. URI.
                "mimeType": "A String", # Required. The IANA standard MIME type of the source data.
              },
              "functionCall": { # A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing the parameters and their values. # Optional. A predicted [FunctionCall] returned from the model that contains a string representing the [FunctionDeclaration.name] with the parameters and their values.
                "args": { # Optional. The function parameters and values in JSON object format. See [FunctionDeclaration.parameters] for parameter details.
                  "a_key": "", # Properties of the object.
                },
                "id": "A String", # Optional. The unique id of the function call. If populated, the client to execute the `function_call` and return the response with the matching `id`.
                "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name].
              },
              "functionResponse": { # The result output from a [FunctionCall] that contains a string representing the [FunctionDeclaration.name] and a structured JSON object containing any output from the function is used as context to the model. This should contain the result of a [FunctionCall] made based on model prediction. # 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.
                "id": "A String", # Optional. The id of the function call this response is for. Populated by the client to match the corresponding function call `id`.
                "name": "A String", # Required. The name of the function to call. Matches [FunctionDeclaration.name] and [FunctionCall.name].
                "response": { # 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.
                  "a_key": "", # Properties of the object.
                },
              },
              "inlineData": { # Content blob. # Optional. Inlined bytes data.
                "data": "A String", # Required. Raw bytes.
                "displayName": "A String", # Optional. Display name of the blob. Used to provide a label or filename to distinguish blobs. This field is only returned in PromptMessage for prompt management. It is currently used in the Gemini GenerateContent calls only when server side tools (code_execution, google_search, and url_context) are enabled.
                "mimeType": "A String", # Required. The IANA standard MIME type of the source data.
              },
              "text": "A String", # Optional. Text part (can be code).
              "thought": True or False, # Optional. Indicates if the part is thought from the model.
              "thoughtSignature": "A String", # Optional. An opaque signature for the thought so it can be reused in subsequent requests.
              "videoMetadata": { # Metadata describes the input video content. # Optional. Video metadata. The metadata should only be specified while the video data is presented in inline_data or file_data.
                "endOffset": "A String", # Optional. The end offset of the video.
                "fps": 3.14, # Optional. The frame rate of the video sent to the model. If not specified, the default value will be 1.0. The fps range is (0.0, 24.0].
                "startOffset": "A String", # Optional. The start offset of the video.
              },
            },
          ],
          "role": "A String", # Optional. The producer of the content. Must be either 'user' or 'model'. Useful to set for multi-turn conversations, otherwise can be left blank or unset.
        },
        "toolConfig": { # Tool config. This config is shared for all tools provided in the request. # Optional. Tool config. This config is shared for all tools provided in the request.
          "functionCallingConfig": { # Function calling config. # Optional. Function calling config.
            "allowedFunctionNames": [ # Optional. Function names to call. Only set when the Mode is ANY. Function names should match [FunctionDeclaration.name]. With mode set to ANY, model will predict a function call from the set of function names provided.
              "A String",
            ],
            "mode": "A String", # Optional. Function calling mode.
          },
          "retrievalConfig": { # Retrieval config. # Optional. Retrieval config.
            "languageCode": "A String", # The language code of the user.
            "latLng": { # An object that represents a latitude/longitude pair. This is expressed as a pair of doubles to represent degrees latitude and degrees longitude. Unless specified otherwise, this object must conform to the WGS84 standard. Values must be within normalized ranges. # The location of the user.
              "latitude": 3.14, # The latitude in degrees. It must be in the range [-90.0, +90.0].
              "longitude": 3.14, # The longitude in degrees. It must be in the range [-180.0, +180.0].
            },
          },
        },
        "tools": [ # Optional. A list of `Tools` the model may use to generate the next response. A `Tool` is a piece of code that enables the system to interact with external systems to perform an action, or set of actions, outside of knowledge and scope of the model.
          { # Tool details that the model may use to generate response. A `Tool` is a piece of code that enables the system to interact with external systems to perform an action, or set of actions, outside of knowledge and scope of the model. A Tool object should contain exactly one type of Tool (e.g FunctionDeclaration, Retrieval or GoogleSearchRetrieval).
            "codeExecution": { # Tool that executes code generated by the model, and automatically returns the result to the model. See also [ExecutableCode]and [CodeExecutionResult] which are input and output to this tool. # Optional. CodeExecution tool type. Enables the model to execute code as part of generation.
            },
            "enterpriseWebSearch": { # Tool to search public web data, powered by Vertex AI Search and Sec4 compliance. # Optional. Tool to support searching public web data, powered by Vertex AI Search and Sec4 compliance.
              "excludeDomains": [ # Optional. List of domains to be excluded from the search results. The default limit is 2000 domains.
                "A String",
              ],
            },
            "functionDeclarations": [ # Optional. Function tool type. One or more function declarations to be passed to the model along with the current user query. Model may decide to call a subset of these functions by populating FunctionCall in the response. User should provide a FunctionResponse for each function call in the next turn. Based on the function responses, Model will generate the final response back to the user. Maximum 512 function declarations can be provided.
              { # Structured representation of a function declaration as defined by the [OpenAPI 3.0 specification](https://spec.openapis.org/oas/v3.0.3). Included in this declaration are the function name, description, parameters and response type. This FunctionDeclaration is a representation of a block of code that can be used as a `Tool` by the model and executed by the client.
                "description": "A String", # Optional. Description and purpose of the function. Model uses it to decide how and whether to call the function.
                "name": "A String", # Required. The name of the function to call. Must start with a letter or an underscore. Must be a-z, A-Z, 0-9, or contain underscores, dots and dashes, with a maximum length of 64.
                "parameters": { # Schema is used to define the format of input/output data. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema-object). More fields may be added in the future as needed. # Optional. Describes the parameters to this function in JSON Schema Object format. Reflects the Open API 3.03 Parameter Object. string Key: the name of the parameter. Parameter names are case sensitive. Schema Value: the Schema defining the type used for the parameter. For function with no parameters, this can be left unset. Parameter names must start with a letter or an underscore and must only contain chars a-z, A-Z, 0-9, or underscores with a maximum length of 64. Example with 1 required and 1 optional parameter: type: OBJECT properties: param1: type: STRING param2: type: INTEGER required: - param1
                  "additionalProperties": "", # Optional. Can either be a boolean or an object; controls the presence of additional properties.
                  "anyOf": [ # Optional. The value should be validated against any (one or more) of the subschemas in the list.
                    # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                  ],
                  "default": "", # Optional. Default value of the data.
                  "defs": { # Optional. A map of definitions for use by `ref` Only allowed at the root of the schema.
                    "a_key": # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                  },
                  "description": "A String", # Optional. The description of the data.
                  "enum": [ # Optional. Possible values of the element of primitive type with enum format. Examples: 1. We can define direction as : {type:STRING, format:enum, enum:["EAST", NORTH", "SOUTH", "WEST"]} 2. We can define apartment number as : {type:INTEGER, format:enum, enum:["101", "201", "301"]}
                    "A String",
                  ],
                  "example": "", # Optional. Example of the object. Will only populated when the object is the root.
                  "format": "A String", # Optional. The format of the data. Supported formats: for NUMBER type: "float", "double" for INTEGER type: "int32", "int64" for STRING type: "email", "byte", etc
                  "items": # Object with schema name: GoogleCloudAiplatformV1beta1Schema # Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY.
                  "maxItems": "A String", # Optional. Maximum number of the elements for Type.ARRAY.
                  "maxLength": "A String", # Optional. Maximum length of the Type.STRING
                  "maxProperties": "A String", # Optional. Maximum number of the properties for Type.OBJECT.
                  "maximum": 3.14, # Optional. Maximum value of the Type.INTEGER and Type.NUMBER
                  "minItems": "A String", # Optional. Minimum number of the elements for Type.ARRAY.
                  "minLength": "A String", # Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING
                  "minProperties": "A String", # Optional. Minimum number of the properties for Type.OBJECT.
                  "minimum": 3.14, # Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER
                  "nullable": True or False, # Optional. Indicates if the value may be null.
                  "pattern": "A String", # Optional. Pattern of the Type.STRING to restrict a string to a regular expression.
                  "properties": { # Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT.
                    "a_key": # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                  },
                  "propertyOrdering": [ # Optional. The order of the properties. Not a standard field in open api spec. Only used to support the order of the properties.
                    "A String",
                  ],
                  "ref": "A String", # Optional. Allows indirect references between schema nodes. The value should be a valid reference to a child of the root `defs`. For example, the following schema defines a reference to a schema node named "Pet": type: object properties: pet: ref: #/defs/Pet defs: Pet: type: object properties: name: type: string The value of the "pet" property is a reference to the schema node named "Pet". See details in https://json-schema.org/understanding-json-schema/structuring
                  "required": [ # Optional. Required properties of Type.OBJECT.
                    "A String",
                  ],
                  "title": "A String", # Optional. The title of the Schema.
                  "type": "A String", # Optional. The type of the data.
                },
                "parametersJsonSchema": "", # Optional. Describes the parameters to the function in JSON Schema format. The schema must describe an object where the properties are the parameters to the function. For example: ``` { "type": "object", "properties": { "name": { "type": "string" }, "age": { "type": "integer" } }, "additionalProperties": false, "required": ["name", "age"], "propertyOrdering": ["name", "age"] } ``` This field is mutually exclusive with `parameters`.
                "response": { # Schema is used to define the format of input/output data. Represents a select subset of an [OpenAPI 3.0 schema object](https://spec.openapis.org/oas/v3.0.3#schema-object). More fields may be added in the future as needed. # Optional. Describes the output from this function in JSON Schema format. Reflects the Open API 3.03 Response Object. The Schema defines the type used for the response value of the function.
                  "additionalProperties": "", # Optional. Can either be a boolean or an object; controls the presence of additional properties.
                  "anyOf": [ # Optional. The value should be validated against any (one or more) of the subschemas in the list.
                    # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                  ],
                  "default": "", # Optional. Default value of the data.
                  "defs": { # Optional. A map of definitions for use by `ref` Only allowed at the root of the schema.
                    "a_key": # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                  },
                  "description": "A String", # Optional. The description of the data.
                  "enum": [ # Optional. Possible values of the element of primitive type with enum format. Examples: 1. We can define direction as : {type:STRING, format:enum, enum:["EAST", NORTH", "SOUTH", "WEST"]} 2. We can define apartment number as : {type:INTEGER, format:enum, enum:["101", "201", "301"]}
                    "A String",
                  ],
                  "example": "", # Optional. Example of the object. Will only populated when the object is the root.
                  "format": "A String", # Optional. The format of the data. Supported formats: for NUMBER type: "float", "double" for INTEGER type: "int32", "int64" for STRING type: "email", "byte", etc
                  "items": # Object with schema name: GoogleCloudAiplatformV1beta1Schema # Optional. SCHEMA FIELDS FOR TYPE ARRAY Schema of the elements of Type.ARRAY.
                  "maxItems": "A String", # Optional. Maximum number of the elements for Type.ARRAY.
                  "maxLength": "A String", # Optional. Maximum length of the Type.STRING
                  "maxProperties": "A String", # Optional. Maximum number of the properties for Type.OBJECT.
                  "maximum": 3.14, # Optional. Maximum value of the Type.INTEGER and Type.NUMBER
                  "minItems": "A String", # Optional. Minimum number of the elements for Type.ARRAY.
                  "minLength": "A String", # Optional. SCHEMA FIELDS FOR TYPE STRING Minimum length of the Type.STRING
                  "minProperties": "A String", # Optional. Minimum number of the properties for Type.OBJECT.
                  "minimum": 3.14, # Optional. SCHEMA FIELDS FOR TYPE INTEGER and NUMBER Minimum value of the Type.INTEGER and Type.NUMBER
                  "nullable": True or False, # Optional. Indicates if the value may be null.
                  "pattern": "A String", # Optional. Pattern of the Type.STRING to restrict a string to a regular expression.
                  "properties": { # Optional. SCHEMA FIELDS FOR TYPE OBJECT Properties of Type.OBJECT.
                    "a_key": # Object with schema name: GoogleCloudAiplatformV1beta1Schema
                  },
                  "propertyOrdering": [ # Optional. The order of the properties. Not a standard field in open api spec. Only used to support the order of the properties.
                    "A String",
                  ],
                  "ref": "A String", # Optional. Allows indirect references between schema nodes. The value should be a valid reference to a child of the root `defs`. For example, the following schema defines a reference to a schema node named "Pet": type: object properties: pet: ref: #/defs/Pet defs: Pet: type: object properties: name: type: string The value of the "pet" property is a reference to the schema node named "Pet". See details in https://json-schema.org/understanding-json-schema/structuring
                  "required": [ # Optional. Required properties of Type.OBJECT.
                    "A String",
                  ],
                  "title": "A String", # Optional. The title of the Schema.
                  "type": "A String", # Optional. The type of the data.
                },
                "responseJsonSchema": "", # Optional. Describes the output from this function in JSON Schema format. The value specified by the schema is the response value of the function. This field is mutually exclusive with `response`.
              },
            ],
            "googleMaps": { # Tool to retrieve public maps data for grounding, powered by Google. # Optional. GoogleMaps tool type. Tool to support Google Maps in Model.
              "enableWidget": True or False, # Optional. If true, include the widget context token in the response.
            },
            "googleSearch": { # GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google. # Optional. GoogleSearch tool type. Tool to support Google Search in Model. Powered by Google.
              "excludeDomains": [ # Optional. List of domains to be excluded from the search results. The default limit is 2000 domains. Example: ["amazon.com", "facebook.com"].
                "A String",
              ],
            },
            "googleSearchRetrieval": { # Tool to retrieve public web data for grounding, powered by Google. # Optional. Specialized retrieval tool that is powered by Google Search.
              "dynamicRetrievalConfig": { # Describes the options to customize dynamic retrieval. # Specifies the dynamic retrieval configuration for the given source.
                "dynamicThreshold": 3.14, # Optional. The threshold to be used in dynamic retrieval. If not set, a system default value is used.
                "mode": "A String", # The mode of the predictor to be used in dynamic retrieval.
              },
            },
            "retrieval": { # Defines a retrieval tool that model can call to access external knowledge. # Optional. Retrieval tool type. System will always execute the provided retrieval tool(s) to get external knowledge to answer the prompt. Retrieval results are presented to the model for generation.
              "disableAttribution": True or False, # Optional. Deprecated. This option is no longer supported.
              "externalApi": { # Retrieve from data source powered by external API for grounding. The external API is not owned by Google, but need to follow the pre-defined API spec. # Use data source powered by external API for grounding.
                "apiAuth": { # The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. # The authentication config to access the API. Deprecated. Please use auth_config instead.
                  "apiKeyConfig": { # The API secret. # The API secret.
                    "apiKeySecretVersion": "A String", # Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version}
                    "apiKeyString": "A String", # The API key string. Either this or `api_key_secret_version` must be set.
                  },
                },
                "apiSpec": "A String", # The API spec that the external API implements.
                "authConfig": { # Auth configuration to run the extension. # The authentication config to access the API.
                  "apiKeyConfig": { # Config for authentication with API key. # Config for API key auth.
                    "apiKeySecret": "A String", # Optional. The name of the SecretManager secret version resource storing the API key. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If both `api_key_secret` and `api_key_string` are specified, this field takes precedence over `api_key_string`. - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
                    "apiKeyString": "A String", # Optional. The API key to be used in the request directly.
                    "httpElementLocation": "A String", # Optional. The location of the API key.
                    "name": "A String", # Optional. The parameter name of the API key. E.g. If the API request is "https://example.com/act?api_key=", "api_key" would be the parameter name.
                  },
                  "authType": "A String", # Type of auth scheme.
                  "googleServiceAccountConfig": { # Config for Google Service Account Authentication. # Config for Google Service Account auth.
                    "serviceAccount": "A String", # Optional. The service account that the extension execution service runs as. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified service account. - If not specified, the Vertex AI Extension Service Agent will be used to execute the Extension.
                  },
                  "httpBasicAuthConfig": { # Config for HTTP Basic Authentication. # Config for HTTP Basic auth.
                    "credentialSecret": "A String", # Required. The name of the SecretManager secret version resource storing the base64 encoded credentials. Format: `projects/{project}/secrets/{secrete}/versions/{version}` - If specified, the `secretmanager.versions.access` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the specified resource.
                  },
                  "oauthConfig": { # Config for user oauth. # Config for user oauth.
                    "accessToken": "A String", # Access token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
                    "serviceAccount": "A String", # The service account used to generate access tokens for executing the Extension. - If the service account is specified, the `iam.serviceAccounts.getAccessToken` permission should be granted to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents) on the provided service account.
                  },
                  "oidcConfig": { # Config for user OIDC auth. # Config for user OIDC auth.
                    "idToken": "A String", # OpenID Connect formatted ID token for extension endpoint. Only used to propagate token from [[ExecuteExtensionRequest.runtime_auth_config]] at request time.
                    "serviceAccount": "A String", # The service account used to generate an OpenID Connect (OIDC)-compatible JWT token signed by the Google OIDC Provider (accounts.google.com) for extension endpoint (https://cloud.google.com/iam/docs/create-short-lived-credentials-direct#sa-credentials-oidc). - The audience for the token will be set to the URL in the server url defined in the OpenApi spec. - If the service account is provided, the service account should grant `iam.serviceAccounts.getOpenIdToken` permission to Vertex AI Extension Service Agent (https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents).
                  },
                },
                "elasticSearchParams": { # The search parameters to use for the ELASTIC_SEARCH spec. # Parameters for the elastic search API.
                  "index": "A String", # The ElasticSearch index to use.
                  "numHits": 42, # Optional. Number of hits (chunks) to request. When specified, it is passed to Elasticsearch as the `num_hits` param.
                  "searchTemplate": "A String", # The ElasticSearch search template to use.
                },
                "endpoint": "A String", # The endpoint of the external API. The system will call the API at this endpoint to retrieve the data for grounding. Example: https://acme.com:443/search
                "simpleSearchParams": { # The search parameters to use for SIMPLE_SEARCH spec. # Parameters for the simple search API.
                },
              },
              "vertexAiSearch": { # Retrieve from Vertex AI Search datastore or engine for grounding. datastore and engine are mutually exclusive. See https://cloud.google.com/products/agent-builder # Set to use data source powered by Vertex AI Search.
                "dataStoreSpecs": [ # Specifications that define the specific DataStores to be searched, along with configurations for those data stores. This is only considered for Engines with multiple data stores. It should only be set if engine is used.
                  { # Define data stores within engine to filter on in a search call and configurations for those data stores. For more information, see https://cloud.google.com/generative-ai-app-builder/docs/reference/rpc/google.cloud.discoveryengine.v1#datastorespec
                    "dataStore": "A String", # Full resource name of DataStore, such as Format: `projects/{project}/locations/{location}/collections/{collection}/dataStores/{dataStore}`
                    "filter": "A String", # Optional. Filter specification to filter documents in the data store specified by data_store field. For more information on filtering, see [Filtering](https://cloud.google.com/generative-ai-app-builder/docs/filter-search-metadata)
                  },
                ],
                "datastore": "A String", # Optional. Fully-qualified Vertex AI Search data store resource ID. Format: `projects/{project}/locations/{location}/collections/{collection}/dataStores/{dataStore}`
                "engine": "A String", # Optional. Fully-qualified Vertex AI Search engine resource ID. Format: `projects/{project}/locations/{location}/collections/{collection}/engines/{engine}`
                "filter": "A String", # Optional. Filter strings to be passed to the search API.
                "maxResults": 42, # Optional. Number of search results to return per query. The default value is 10. The maximumm allowed value is 10.
              },
              "vertexRagStore": { # Retrieve from Vertex RAG Store for grounding. # Set to use data source powered by Vertex RAG store. User data is uploaded via the VertexRagDataService.
                "ragCorpora": [ # Optional. Deprecated. Please use rag_resources instead.
                  "A String",
                ],
                "ragResources": [ # Optional. The representation of the rag source. It can be used to specify corpus only or ragfiles. Currently only support one corpus or multiple files from one corpus. In the future we may open up multiple corpora support.
                  { # The definition of the Rag resource.
                    "ragCorpus": "A String", # Optional. RagCorpora resource name. Format: `projects/{project}/locations/{location}/ragCorpora/{rag_corpus}`
                    "ragFileIds": [ # Optional. rag_file_id. The files should be in the same rag_corpus set in rag_corpus field.
                      "A String",
                    ],
                  },
                ],
                "ragRetrievalConfig": { # Specifies the context retrieval config. # Optional. The retrieval config for the Rag query.
                  "filter": { # Config for filters. # Optional. Config for filters.
                    "metadataFilter": "A String", # Optional. String for metadata filtering.
                    "vectorDistanceThreshold": 3.14, # Optional. Only returns contexts with vector distance smaller than the threshold.
                    "vectorSimilarityThreshold": 3.14, # Optional. Only returns contexts with vector similarity larger than the threshold.
                  },
                  "hybridSearch": { # Config for Hybrid Search. # Optional. Config for Hybrid Search.
                    "alpha": 3.14, # Optional. Alpha value controls the weight between dense and sparse vector search results. The range is [0, 1], while 0 means sparse vector search only and 1 means dense vector search only. The default value is 0.5 which balances sparse and dense vector search equally.
                  },
                  "ranking": { # Config for ranking and reranking. # Optional. Config for ranking and reranking.
                    "llmRanker": { # Config for LlmRanker. # Optional. Config for LlmRanker.
                      "modelName": "A String", # Optional. The model name used for ranking. See [Supported models](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference#supported-models).
                    },
                    "rankService": { # Config for Rank Service. # Optional. Config for Rank Service.
                      "modelName": "A String", # Optional. The model name of the rank service. Format: `semantic-ranker-512@latest`
                    },
                  },
                  "topK": 42, # Optional. The number of contexts to retrieve.
                },
                "similarityTopK": 42, # Optional. Number of top k results to return from the selected corpora.
                "storeContext": True or False, # Optional. Currently only supported for Gemini Multimodal Live API. In Gemini Multimodal Live API, if `store_context` bool is specified, Gemini will leverage it to automatically memorize the interactions between the client and Gemini, and retrieve context when needed to augment the response generation for users' ongoing and future interactions.
                "vectorDistanceThreshold": 3.14, # Optional. Only return results with vector distance smaller than the threshold.
              },
            },
            "urlContext": { # Tool to support URL context. # Optional. Tool to support URL context retrieval.
            },
          },
        ],
      },
    },
  },
  "tuningResourceUsageAssessmentConfig": { # Configuration for the tuning resource usage assessment. # Optional. Configuration for the tuning resource usage assessment.
    "modelName": "A String", # Required. The name of the model used for tuning.
  },
  "tuningValidationAssessmentConfig": { # Configuration for the tuning validation assessment. # Optional. Configuration for the tuning validation assessment.
    "datasetUsage": "A String", # Required. The dataset usage (e.g. training/validation).
    "modelName": "A String", # Required. The name of the model used for tuning.
  },
}

  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # This resource represents a long-running operation that is the result of a network API call.
  "done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
  "error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
    "code": 42, # The status code, which should be an enum value of google.rpc.Code.
    "details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
      {
        "a_key": "", # Properties of the object. Contains field @type with type URL.
      },
    ],
    "message": "A String", # 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.
  },
  "metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
    "a_key": "", # Properties of the object. Contains field @type with type URL.
  },
  "name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
  "response": { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
    "a_key": "", # Properties of the object. Contains field @type with type URL.
  },
}

close()

Close httplib2 connections.

create(parent, body=None, x__xgafv=None)

Creates a Dataset.

Args:
  parent: string, Required. The resource name of the Location to create the Dataset in. Format: `projects/{project}/locations/{location}` (required)
  body: object, The request body.
    The object takes the form of:

{ # A collection of DataItems and Annotations on them.
  "createTime": "A String", # Output only. Timestamp when this Dataset was created.
  "dataItemCount": "A String", # Output only. The number of DataItems in this Dataset. Only apply for non-structured Dataset.
  "description": "A String", # The description of the Dataset.
  "displayName": "A String", # Required. The user-defined name of the Dataset. The name can be up to 128 characters long and can consist of any UTF-8 characters.
  "encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Customer-managed encryption key spec for a Dataset. If set, this Dataset and all sub-resources of this Dataset will be secured by this key.
    "kmsKeyName": "A String", # 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.
  },
  "etag": "A String", # Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
  "labels": { # The labels with user-defined metadata to organize your Datasets. 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. No more than 64 user labels can be associated with one Dataset (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each Dataset: * "aiplatform.googleapis.com/dataset_metadata_schema": output only, its value is the metadata_schema's title.
    "a_key": "A String",
  },
  "metadata": "", # Required. Additional information about the Dataset.
  "metadataArtifact": "A String", # Output only. The resource name of the Artifact that was created in MetadataStore when creating the Dataset. The Artifact resource name pattern is `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
  "metadataSchemaUri": "A String", # Required. Points to a YAML file stored on Google Cloud Storage describing additional information about the Dataset. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/metadata/.
  "modelReference": "A String", # Optional. Reference to the public base model last used by the dataset. Only set for prompt datasets.
  "name": "A String", # Output only. Identifier. The resource name of the Dataset. Format: `projects/{project}/locations/{location}/datasets/{dataset}`
  "satisfiesPzi": True or False, # Output only. Reserved for future use.
  "satisfiesPzs": True or False, # Output only. Reserved for future use.
  "savedQueries": [ # All SavedQueries belong to the Dataset will be returned in List/Get Dataset response. The annotation_specs field will not be populated except for UI cases which will only use annotation_spec_count. In CreateDataset request, a SavedQuery is created together if this field is set, up to one SavedQuery can be set in CreateDatasetRequest. The SavedQuery should not contain any AnnotationSpec.
    { # A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters.
      "annotationFilter": "A String", # Output only. Filters on the Annotations in the dataset.
      "annotationSpecCount": 42, # Output only. Number of AnnotationSpecs in the context of the SavedQuery.
      "createTime": "A String", # Output only. Timestamp when this SavedQuery was created.
      "displayName": "A String", # Required. The user-defined name of the SavedQuery. The name can be up to 128 characters long and can consist of any UTF-8 characters.
      "etag": "A String", # Used to perform a consistent read-modify-write update. If not set, a blind "overwrite" update happens.
      "metadata": "", # Some additional information about the SavedQuery.
      "name": "A String", # Output only. Resource name of the SavedQuery.
      "problemType": "A String", # Required. Problem type of the SavedQuery. Allowed values: * IMAGE_CLASSIFICATION_SINGLE_LABEL * IMAGE_CLASSIFICATION_MULTI_LABEL * IMAGE_BOUNDING_POLY * IMAGE_BOUNDING_BOX * TEXT_CLASSIFICATION_SINGLE_LABEL * TEXT_CLASSIFICATION_MULTI_LABEL * TEXT_EXTRACTION * TEXT_SENTIMENT * VIDEO_CLASSIFICATION * VIDEO_OBJECT_TRACKING
      "supportAutomlTraining": True or False, # Output only. If the Annotations belonging to the SavedQuery can be used for AutoML training.
      "updateTime": "A String", # Output only. Timestamp when SavedQuery was last updated.
    },
  ],
  "updateTime": "A String", # Output only. Timestamp when this Dataset was last updated.
}

  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # This resource represents a long-running operation that is the result of a network API call.
  "done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
  "error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
    "code": 42, # The status code, which should be an enum value of google.rpc.Code.
    "details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
      {
        "a_key": "", # Properties of the object. Contains field @type with type URL.
      },
    ],
    "message": "A String", # 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.
  },
  "metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
    "a_key": "", # Properties of the object. Contains field @type with type URL.
  },
  "name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
  "response": { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
    "a_key": "", # Properties of the object. Contains field @type with type URL.
  },
}

delete(name, x__xgafv=None)

Deletes a Dataset.

Args:
  name: string, Required. The resource name of the Dataset to delete. Format: `projects/{project}/locations/{location}/datasets/{dataset}` (required)
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # This resource represents a long-running operation that is the result of a network API call.
  "done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
  "error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
    "code": 42, # The status code, which should be an enum value of google.rpc.Code.
    "details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
      {
        "a_key": "", # Properties of the object. Contains field @type with type URL.
      },
    ],
    "message": "A String", # 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.
  },
  "metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
    "a_key": "", # Properties of the object. Contains field @type with type URL.
  },
  "name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
  "response": { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
    "a_key": "", # Properties of the object. Contains field @type with type URL.
  },
}

export(name, body=None, x__xgafv=None)

Exports data from a Dataset.

Args:
  name: string, Required. The name of the Dataset resource. Format: `projects/{project}/locations/{location}/datasets/{dataset}` (required)
  body: object, The request body.
    The object takes the form of:

{ # Request message for DatasetService.ExportData.
  "exportConfig": { # Describes what part of the Dataset is to be exported, the destination of the export and how to export. # Required. The desired output location.
    "annotationsFilter": "A String", # An expression for filtering what part of the Dataset is to be exported. Only Annotations that match this filter will be exported. The filter syntax is the same as in ListAnnotations.
    "fractionSplit": { # Assigns the input data to training, validation, and test sets as per the given fractions. Any of `training_fraction`, `validation_fraction` and `test_fraction` may optionally be provided, they must sum to up to 1. If the provided ones sum to less than 1, the remainder is assigned to sets as decided by Vertex AI. If none of the fractions are set, by default roughly 80% of data is used for training, 10% for validation, and 10% for test. # Split based on fractions defining the size of each set.
      "testFraction": 3.14, # The fraction of the input data that is to be used to evaluate the Model.
      "trainingFraction": 3.14, # The fraction of the input data that is to be used to train the Model.
      "validationFraction": 3.14, # The fraction of the input data that is to be used to validate the Model.
    },
    "gcsDestination": { # The Google Cloud Storage location where the output is to be written to. # The Google Cloud Storage location where the output is to be written to. In the given directory a new directory will be created with name: `export-data--` where timestamp is in YYYY-MM-DDThh:mm:ss.sssZ ISO-8601 format. All export output will be written into that directory. Inside that directory, annotations with the same schema will be grouped into sub directories which are named with the corresponding annotations' schema title. Inside these sub directories, a schema.yaml will be created to describe the output format.
      "outputUriPrefix": "A String", # 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.
    },
  },
}

  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # This resource represents a long-running operation that is the result of a network API call.
  "done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
  "error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
    "code": 42, # The status code, which should be an enum value of google.rpc.Code.
    "details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
      {
        "a_key": "", # Properties of the object. Contains field @type with type URL.
      },
    ],
    "message": "A String", # 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.
  },
  "metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
    "a_key": "", # Properties of the object. Contains field @type with type URL.
  },
  "name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
  "response": { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
    "a_key": "", # Properties of the object. Contains field @type with type URL.
  },
}

get(name, readMask=None, x__xgafv=None)

Gets a Dataset.

Args:
  name: string, Required. The name of the Dataset resource. (required)
  readMask: string, Mask specifying which fields to read.
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # A collection of DataItems and Annotations on them.
  "createTime": "A String", # Output only. Timestamp when this Dataset was created.
  "dataItemCount": "A String", # Output only. The number of DataItems in this Dataset. Only apply for non-structured Dataset.
  "description": "A String", # The description of the Dataset.
  "displayName": "A String", # Required. The user-defined name of the Dataset. The name can be up to 128 characters long and can consist of any UTF-8 characters.
  "encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Customer-managed encryption key spec for a Dataset. If set, this Dataset and all sub-resources of this Dataset will be secured by this key.
    "kmsKeyName": "A String", # 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.
  },
  "etag": "A String", # Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
  "labels": { # The labels with user-defined metadata to organize your Datasets. 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. No more than 64 user labels can be associated with one Dataset (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each Dataset: * "aiplatform.googleapis.com/dataset_metadata_schema": output only, its value is the metadata_schema's title.
    "a_key": "A String",
  },
  "metadata": "", # Required. Additional information about the Dataset.
  "metadataArtifact": "A String", # Output only. The resource name of the Artifact that was created in MetadataStore when creating the Dataset. The Artifact resource name pattern is `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
  "metadataSchemaUri": "A String", # Required. Points to a YAML file stored on Google Cloud Storage describing additional information about the Dataset. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/metadata/.
  "modelReference": "A String", # Optional. Reference to the public base model last used by the dataset. Only set for prompt datasets.
  "name": "A String", # Output only. Identifier. The resource name of the Dataset. Format: `projects/{project}/locations/{location}/datasets/{dataset}`
  "satisfiesPzi": True or False, # Output only. Reserved for future use.
  "satisfiesPzs": True or False, # Output only. Reserved for future use.
  "savedQueries": [ # All SavedQueries belong to the Dataset will be returned in List/Get Dataset response. The annotation_specs field will not be populated except for UI cases which will only use annotation_spec_count. In CreateDataset request, a SavedQuery is created together if this field is set, up to one SavedQuery can be set in CreateDatasetRequest. The SavedQuery should not contain any AnnotationSpec.
    { # A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters.
      "annotationFilter": "A String", # Output only. Filters on the Annotations in the dataset.
      "annotationSpecCount": 42, # Output only. Number of AnnotationSpecs in the context of the SavedQuery.
      "createTime": "A String", # Output only. Timestamp when this SavedQuery was created.
      "displayName": "A String", # Required. The user-defined name of the SavedQuery. The name can be up to 128 characters long and can consist of any UTF-8 characters.
      "etag": "A String", # Used to perform a consistent read-modify-write update. If not set, a blind "overwrite" update happens.
      "metadata": "", # Some additional information about the SavedQuery.
      "name": "A String", # Output only. Resource name of the SavedQuery.
      "problemType": "A String", # Required. Problem type of the SavedQuery. Allowed values: * IMAGE_CLASSIFICATION_SINGLE_LABEL * IMAGE_CLASSIFICATION_MULTI_LABEL * IMAGE_BOUNDING_POLY * IMAGE_BOUNDING_BOX * TEXT_CLASSIFICATION_SINGLE_LABEL * TEXT_CLASSIFICATION_MULTI_LABEL * TEXT_EXTRACTION * TEXT_SENTIMENT * VIDEO_CLASSIFICATION * VIDEO_OBJECT_TRACKING
      "supportAutomlTraining": True or False, # Output only. If the Annotations belonging to the SavedQuery can be used for AutoML training.
      "updateTime": "A String", # Output only. Timestamp when SavedQuery was last updated.
    },
  ],
  "updateTime": "A String", # Output only. Timestamp when this Dataset was last updated.
}

import_(name, body=None, x__xgafv=None)

Imports data into a Dataset.

Args:
  name: string, Required. The name of the Dataset resource. Format: `projects/{project}/locations/{location}/datasets/{dataset}` (required)
  body: object, The request body.
    The object takes the form of:

{ # Request message for DatasetService.ImportData.
  "importConfigs": [ # Required. The desired input locations. The contents of all input locations will be imported in one batch.
    { # Describes the location from where we import data into a Dataset, together with the labels that will be applied to the DataItems and the Annotations.
      "annotationLabels": { # Labels that will be applied to newly imported Annotations. If two Annotations are identical, one of them will be deduped. Two Annotations are considered identical if their payload, payload_schema_uri and all of their labels are the same. These labels will be overridden by Annotation labels specified inside index file referenced by import_schema_uri, e.g. jsonl file.
        "a_key": "A String",
      },
      "dataItemLabels": { # Labels that will be applied to newly imported DataItems. If an identical DataItem as one being imported already exists in the Dataset, then these labels will be appended to these of the already existing one, and if labels with identical key is imported before, the old label value will be overwritten. If two DataItems are identical in the same import data operation, the labels will be combined and if key collision happens in this case, one of the values will be picked randomly. Two DataItems are considered identical if their content bytes are identical (e.g. image bytes or pdf bytes). These labels will be overridden by Annotation labels specified inside index file referenced by import_schema_uri, e.g. jsonl file.
        "a_key": "A String",
      },
      "gcsSource": { # The Google Cloud Storage location for the input content. # The Google Cloud Storage location for the input content.
        "uris": [ # Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/wildcards.
          "A String",
        ],
      },
      "importSchemaUri": "A String", # Required. Points to a YAML file stored on Google Cloud Storage describing the import format. Validation will be done against the schema. The schema is defined as an [OpenAPI 3.0.2 Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject).
    },
  ],
}

  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # This resource represents a long-running operation that is the result of a network API call.
  "done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
  "error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
    "code": 42, # The status code, which should be an enum value of google.rpc.Code.
    "details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
      {
        "a_key": "", # Properties of the object. Contains field @type with type URL.
      },
    ],
    "message": "A String", # 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.
  },
  "metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
    "a_key": "", # Properties of the object. Contains field @type with type URL.
  },
  "name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
  "response": { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
    "a_key": "", # Properties of the object. Contains field @type with type URL.
  },
}

list(parent, filter=None, orderBy=None, pageSize=None, pageToken=None, readMask=None, x__xgafv=None)

Lists Datasets in a Location.

Args:
  parent: string, Required. The name of the Dataset's parent resource. Format: `projects/{project}/locations/{location}` (required)
  filter: string, An expression for filtering the results of the request. For field names both snake_case and camelCase are supported. * `display_name`: supports = and != * `metadata_schema_uri`: supports = and != * `labels` supports general map functions that is: * `labels.key=value` - key:value equality * `labels.key:* or labels:key - key existence * A key including a space must be quoted. `labels."a key"`. Some examples: * `displayName="myDisplayName"` * `labels.myKey="myValue"`
  orderBy: string, A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: * `display_name` * `create_time` * `update_time`
  pageSize: integer, The standard list page size.
  pageToken: string, The standard list page token.
  readMask: string, Mask specifying which fields to read.
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Response message for DatasetService.ListDatasets.
  "datasets": [ # A list of Datasets that matches the specified filter in the request.
    { # A collection of DataItems and Annotations on them.
      "createTime": "A String", # Output only. Timestamp when this Dataset was created.
      "dataItemCount": "A String", # Output only. The number of DataItems in this Dataset. Only apply for non-structured Dataset.
      "description": "A String", # The description of the Dataset.
      "displayName": "A String", # Required. The user-defined name of the Dataset. The name can be up to 128 characters long and can consist of any UTF-8 characters.
      "encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Customer-managed encryption key spec for a Dataset. If set, this Dataset and all sub-resources of this Dataset will be secured by this key.
        "kmsKeyName": "A String", # 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.
      },
      "etag": "A String", # Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
      "labels": { # The labels with user-defined metadata to organize your Datasets. 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. No more than 64 user labels can be associated with one Dataset (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each Dataset: * "aiplatform.googleapis.com/dataset_metadata_schema": output only, its value is the metadata_schema's title.
        "a_key": "A String",
      },
      "metadata": "", # Required. Additional information about the Dataset.
      "metadataArtifact": "A String", # Output only. The resource name of the Artifact that was created in MetadataStore when creating the Dataset. The Artifact resource name pattern is `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
      "metadataSchemaUri": "A String", # Required. Points to a YAML file stored on Google Cloud Storage describing additional information about the Dataset. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/metadata/.
      "modelReference": "A String", # Optional. Reference to the public base model last used by the dataset. Only set for prompt datasets.
      "name": "A String", # Output only. Identifier. The resource name of the Dataset. Format: `projects/{project}/locations/{location}/datasets/{dataset}`
      "satisfiesPzi": True or False, # Output only. Reserved for future use.
      "satisfiesPzs": True or False, # Output only. Reserved for future use.
      "savedQueries": [ # All SavedQueries belong to the Dataset will be returned in List/Get Dataset response. The annotation_specs field will not be populated except for UI cases which will only use annotation_spec_count. In CreateDataset request, a SavedQuery is created together if this field is set, up to one SavedQuery can be set in CreateDatasetRequest. The SavedQuery should not contain any AnnotationSpec.
        { # A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters.
          "annotationFilter": "A String", # Output only. Filters on the Annotations in the dataset.
          "annotationSpecCount": 42, # Output only. Number of AnnotationSpecs in the context of the SavedQuery.
          "createTime": "A String", # Output only. Timestamp when this SavedQuery was created.
          "displayName": "A String", # Required. The user-defined name of the SavedQuery. The name can be up to 128 characters long and can consist of any UTF-8 characters.
          "etag": "A String", # Used to perform a consistent read-modify-write update. If not set, a blind "overwrite" update happens.
          "metadata": "", # Some additional information about the SavedQuery.
          "name": "A String", # Output only. Resource name of the SavedQuery.
          "problemType": "A String", # Required. Problem type of the SavedQuery. Allowed values: * IMAGE_CLASSIFICATION_SINGLE_LABEL * IMAGE_CLASSIFICATION_MULTI_LABEL * IMAGE_BOUNDING_POLY * IMAGE_BOUNDING_BOX * TEXT_CLASSIFICATION_SINGLE_LABEL * TEXT_CLASSIFICATION_MULTI_LABEL * TEXT_EXTRACTION * TEXT_SENTIMENT * VIDEO_CLASSIFICATION * VIDEO_OBJECT_TRACKING
          "supportAutomlTraining": True or False, # Output only. If the Annotations belonging to the SavedQuery can be used for AutoML training.
          "updateTime": "A String", # Output only. Timestamp when SavedQuery was last updated.
        },
      ],
      "updateTime": "A String", # Output only. Timestamp when this Dataset was last updated.
    },
  ],
  "nextPageToken": "A String", # The standard List next-page token.
}

list_next()

Retrieves the next page of results.

        Args:
          previous_request: The request for the previous page. (required)
          previous_response: The response from the request for the previous page. (required)

        Returns:
          A request object that you can call 'execute()' on to request the next
          page. Returns None if there are no more items in the collection.
        

patch(name, body=None, updateMask=None, x__xgafv=None)

Updates a Dataset.

Args:
  name: string, Output only. Identifier. The resource name of the Dataset. Format: `projects/{project}/locations/{location}/datasets/{dataset}` (required)
  body: object, The request body.
    The object takes the form of:

{ # A collection of DataItems and Annotations on them.
  "createTime": "A String", # Output only. Timestamp when this Dataset was created.
  "dataItemCount": "A String", # Output only. The number of DataItems in this Dataset. Only apply for non-structured Dataset.
  "description": "A String", # The description of the Dataset.
  "displayName": "A String", # Required. The user-defined name of the Dataset. The name can be up to 128 characters long and can consist of any UTF-8 characters.
  "encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Customer-managed encryption key spec for a Dataset. If set, this Dataset and all sub-resources of this Dataset will be secured by this key.
    "kmsKeyName": "A String", # 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.
  },
  "etag": "A String", # Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
  "labels": { # The labels with user-defined metadata to organize your Datasets. 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. No more than 64 user labels can be associated with one Dataset (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each Dataset: * "aiplatform.googleapis.com/dataset_metadata_schema": output only, its value is the metadata_schema's title.
    "a_key": "A String",
  },
  "metadata": "", # Required. Additional information about the Dataset.
  "metadataArtifact": "A String", # Output only. The resource name of the Artifact that was created in MetadataStore when creating the Dataset. The Artifact resource name pattern is `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
  "metadataSchemaUri": "A String", # Required. Points to a YAML file stored on Google Cloud Storage describing additional information about the Dataset. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/metadata/.
  "modelReference": "A String", # Optional. Reference to the public base model last used by the dataset. Only set for prompt datasets.
  "name": "A String", # Output only. Identifier. The resource name of the Dataset. Format: `projects/{project}/locations/{location}/datasets/{dataset}`
  "satisfiesPzi": True or False, # Output only. Reserved for future use.
  "satisfiesPzs": True or False, # Output only. Reserved for future use.
  "savedQueries": [ # All SavedQueries belong to the Dataset will be returned in List/Get Dataset response. The annotation_specs field will not be populated except for UI cases which will only use annotation_spec_count. In CreateDataset request, a SavedQuery is created together if this field is set, up to one SavedQuery can be set in CreateDatasetRequest. The SavedQuery should not contain any AnnotationSpec.
    { # A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters.
      "annotationFilter": "A String", # Output only. Filters on the Annotations in the dataset.
      "annotationSpecCount": 42, # Output only. Number of AnnotationSpecs in the context of the SavedQuery.
      "createTime": "A String", # Output only. Timestamp when this SavedQuery was created.
      "displayName": "A String", # Required. The user-defined name of the SavedQuery. The name can be up to 128 characters long and can consist of any UTF-8 characters.
      "etag": "A String", # Used to perform a consistent read-modify-write update. If not set, a blind "overwrite" update happens.
      "metadata": "", # Some additional information about the SavedQuery.
      "name": "A String", # Output only. Resource name of the SavedQuery.
      "problemType": "A String", # Required. Problem type of the SavedQuery. Allowed values: * IMAGE_CLASSIFICATION_SINGLE_LABEL * IMAGE_CLASSIFICATION_MULTI_LABEL * IMAGE_BOUNDING_POLY * IMAGE_BOUNDING_BOX * TEXT_CLASSIFICATION_SINGLE_LABEL * TEXT_CLASSIFICATION_MULTI_LABEL * TEXT_EXTRACTION * TEXT_SENTIMENT * VIDEO_CLASSIFICATION * VIDEO_OBJECT_TRACKING
      "supportAutomlTraining": True or False, # Output only. If the Annotations belonging to the SavedQuery can be used for AutoML training.
      "updateTime": "A String", # Output only. Timestamp when SavedQuery was last updated.
    },
  ],
  "updateTime": "A String", # Output only. Timestamp when this Dataset was last updated.
}

  updateMask: string, Required. The update mask applies to the resource. For the `FieldMask` definition, see google.protobuf.FieldMask. Updatable fields: * `display_name` * `description` * `labels`
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # A collection of DataItems and Annotations on them.
  "createTime": "A String", # Output only. Timestamp when this Dataset was created.
  "dataItemCount": "A String", # Output only. The number of DataItems in this Dataset. Only apply for non-structured Dataset.
  "description": "A String", # The description of the Dataset.
  "displayName": "A String", # Required. The user-defined name of the Dataset. The name can be up to 128 characters long and can consist of any UTF-8 characters.
  "encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Customer-managed encryption key spec for a Dataset. If set, this Dataset and all sub-resources of this Dataset will be secured by this key.
    "kmsKeyName": "A String", # 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.
  },
  "etag": "A String", # Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
  "labels": { # The labels with user-defined metadata to organize your Datasets. 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. No more than 64 user labels can be associated with one Dataset (System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each Dataset: * "aiplatform.googleapis.com/dataset_metadata_schema": output only, its value is the metadata_schema's title.
    "a_key": "A String",
  },
  "metadata": "", # Required. Additional information about the Dataset.
  "metadataArtifact": "A String", # Output only. The resource name of the Artifact that was created in MetadataStore when creating the Dataset. The Artifact resource name pattern is `projects/{project}/locations/{location}/metadataStores/{metadata_store}/artifacts/{artifact}`.
  "metadataSchemaUri": "A String", # Required. Points to a YAML file stored on Google Cloud Storage describing additional information about the Dataset. The schema is defined as an OpenAPI 3.0.2 Schema Object. The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/metadata/.
  "modelReference": "A String", # Optional. Reference to the public base model last used by the dataset. Only set for prompt datasets.
  "name": "A String", # Output only. Identifier. The resource name of the Dataset. Format: `projects/{project}/locations/{location}/datasets/{dataset}`
  "satisfiesPzi": True or False, # Output only. Reserved for future use.
  "satisfiesPzs": True or False, # Output only. Reserved for future use.
  "savedQueries": [ # All SavedQueries belong to the Dataset will be returned in List/Get Dataset response. The annotation_specs field will not be populated except for UI cases which will only use annotation_spec_count. In CreateDataset request, a SavedQuery is created together if this field is set, up to one SavedQuery can be set in CreateDatasetRequest. The SavedQuery should not contain any AnnotationSpec.
    { # A SavedQuery is a view of the dataset. It references a subset of annotations by problem type and filters.
      "annotationFilter": "A String", # Output only. Filters on the Annotations in the dataset.
      "annotationSpecCount": 42, # Output only. Number of AnnotationSpecs in the context of the SavedQuery.
      "createTime": "A String", # Output only. Timestamp when this SavedQuery was created.
      "displayName": "A String", # Required. The user-defined name of the SavedQuery. The name can be up to 128 characters long and can consist of any UTF-8 characters.
      "etag": "A String", # Used to perform a consistent read-modify-write update. If not set, a blind "overwrite" update happens.
      "metadata": "", # Some additional information about the SavedQuery.
      "name": "A String", # Output only. Resource name of the SavedQuery.
      "problemType": "A String", # Required. Problem type of the SavedQuery. Allowed values: * IMAGE_CLASSIFICATION_SINGLE_LABEL * IMAGE_CLASSIFICATION_MULTI_LABEL * IMAGE_BOUNDING_POLY * IMAGE_BOUNDING_BOX * TEXT_CLASSIFICATION_SINGLE_LABEL * TEXT_CLASSIFICATION_MULTI_LABEL * TEXT_EXTRACTION * TEXT_SENTIMENT * VIDEO_CLASSIFICATION * VIDEO_OBJECT_TRACKING
      "supportAutomlTraining": True or False, # Output only. If the Annotations belonging to the SavedQuery can be used for AutoML training.
      "updateTime": "A String", # Output only. Timestamp when SavedQuery was last updated.
    },
  ],
  "updateTime": "A String", # Output only. Timestamp when this Dataset was last updated.
}

searchDataItems(dataset, annotationFilters=None, annotationsFilter=None, annotationsLimit=None, dataItemFilter=None, dataLabelingJob=None, fieldMask=None, orderBy=None, orderByAnnotation_orderBy=None, orderByAnnotation_savedQuery=None, orderByDataItem=None, pageSize=None, pageToken=None, savedQuery=None, x__xgafv=None)

Searches DataItems in a Dataset.

Args:
  dataset: string, Required. The resource name of the Dataset from which to search DataItems. Format: `projects/{project}/locations/{location}/datasets/{dataset}` (required)
  annotationFilters: string, An expression that specifies what Annotations will be returned per DataItem. Annotations satisfied either of the conditions will be returned. * `annotation_spec_id` - for = or !=. Must specify `saved_query_id=` - saved query id that annotations should belong to. (repeated)
  annotationsFilter: string, An expression for filtering the Annotations that will be returned per DataItem. * `annotation_spec_id` - for = or !=.
  annotationsLimit: integer, If set, only up to this many of Annotations will be returned per DataItemView. The maximum value is 1000. If not set, the maximum value will be used.
  dataItemFilter: string, An expression for filtering the DataItem that will be returned. * `data_item_id` - for = or !=. * `labeled` - for = or !=. * `has_annotation(ANNOTATION_SPEC_ID)` - true only for DataItem that have at least one annotation with annotation_spec_id = `ANNOTATION_SPEC_ID` in the context of SavedQuery or DataLabelingJob. For example: * `data_item=1` * `has_annotation(5)`
  dataLabelingJob: string, The resource name of a DataLabelingJob. Format: `projects/{project}/locations/{location}/dataLabelingJobs/{data_labeling_job}` If this field is set, all of the search will be done in the context of this DataLabelingJob.
  fieldMask: string, Mask specifying which fields of DataItemView to read.
  orderBy: string, A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending.
  orderByAnnotation_orderBy: string, A comma-separated list of annotation fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Must also specify saved_query.
  orderByAnnotation_savedQuery: string, Required. Saved query of the Annotation. Only Annotations belong to this saved query will be considered for ordering.
  orderByDataItem: string, A comma-separated list of data item fields to order by, sorted in ascending order. Use "desc" after a field name for descending.
  pageSize: integer, Requested page size. Server may return fewer results than requested. Default and maximum page size is 100.
  pageToken: string, A token identifying a page of results for the server to return Typically obtained via SearchDataItemsResponse.next_page_token of the previous DatasetService.SearchDataItems call.
  savedQuery: string, The resource name of a SavedQuery(annotation set in UI). Format: `projects/{project}/locations/{location}/datasets/{dataset}/savedQueries/{saved_query}` All of the search will be done in the context of this SavedQuery.
  x__xgafv: string, V1 error format.
    Allowed values
      1 - v1 error format
      2 - v2 error format

Returns:
  An object of the form:

    { # Response message for DatasetService.SearchDataItems.
  "dataItemViews": [ # The DataItemViews read.
    { # A container for a single DataItem and Annotations on it.
      "annotations": [ # The Annotations on the DataItem. If too many Annotations should be returned for the DataItem, this field will be truncated per annotations_limit in request. If it was, then the has_truncated_annotations will be set to true.
        { # Used to assign specific AnnotationSpec to a particular area of a DataItem or the whole part of the DataItem.
          "annotationSource": { # References an API call. It contains more information about long running operation and Jobs that are triggered by the API call. # Output only. The source of the Annotation.
            "dataLabelingJob": "A String", # For API calls that start a LabelingJob. Resource name of the LabelingJob. Format: `projects/{project}/locations/{location}/dataLabelingJobs/{data_labeling_job}`
            "method": "A String", # The method name of the API RPC call. For example, "/google.cloud.aiplatform.{apiVersion}.DatasetService.CreateDataset"
            "operation": "A String", # For API calls that return a long running operation. Resource name of the long running operation. Format: `projects/{project}/locations/{location}/operations/{operation}`
          },
          "createTime": "A String", # Output only. Timestamp when this Annotation was created.
          "etag": "A String", # Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
          "labels": { # Optional. The labels with user-defined metadata to organize your Annotations. 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. No more than 64 user labels can be associated with one Annotation(System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. Following system labels exist for each Annotation: * "aiplatform.googleapis.com/annotation_set_name": optional, name of the UI's annotation set this Annotation belongs to. If not set, the Annotation is not visible in the UI. * "aiplatform.googleapis.com/payload_schema": output only, its value is the payload_schema's title.
            "a_key": "A String",
          },
          "name": "A String", # Output only. Resource name of the Annotation.
          "payload": "", # Required. The schema of the payload can be found in payload_schema.
          "payloadSchemaUri": "A String", # Required. Google Cloud Storage URI points to a YAML file describing payload. The schema is defined as an [OpenAPI 3.0.2 Schema Object](https://github.com/OAI/OpenAPI-Specification/blob/main/versions/3.0.2.md#schemaObject). The schema files that can be used here are found in gs://google-cloud-aiplatform/schema/dataset/annotation/, note that the chosen schema must be consistent with the parent Dataset's metadata.
          "updateTime": "A String", # Output only. Timestamp when this Annotation was last updated.
        },
      ],
      "dataItem": { # A piece of data in a Dataset. Could be an image, a video, a document or plain text. # The DataItem.
        "createTime": "A String", # Output only. Timestamp when this DataItem was created.
        "etag": "A String", # Optional. Used to perform consistent read-modify-write updates. If not set, a blind "overwrite" update happens.
        "labels": { # Optional. The labels with user-defined metadata to organize your DataItems. 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. No more than 64 user labels can be associated with one DataItem(System labels are excluded). See https://goo.gl/xmQnxf for more information and examples of labels. System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
          "a_key": "A String",
        },
        "name": "A String", # Output only. The resource name of the DataItem.
        "payload": "", # Required. The data that the DataItem represents (for example, an image or a text snippet). The schema of the payload is stored in the parent Dataset's metadata schema's dataItemSchemaUri field.
        "satisfiesPzi": True or False, # Output only. Reserved for future use.
        "satisfiesPzs": True or False, # Output only. Reserved for future use.
        "updateTime": "A String", # Output only. Timestamp when this DataItem was last updated.
      },
      "hasTruncatedAnnotations": True or False, # True if and only if the Annotations field has been truncated. It happens if more Annotations for this DataItem met the request's annotation_filter than are allowed to be returned by annotations_limit. Note that if Annotations field is not being returned due to field mask, then this field will not be set to true no matter how many Annotations are there.
    },
  ],
  "nextPageToken": "A String", # A token to retrieve next page of results. Pass to SearchDataItemsRequest.page_token to obtain that page.
}

searchDataItems_next()

Retrieves the next page of results.

        Args:
          previous_request: The request for the previous page. (required)
          previous_response: The response from the request for the previous page. (required)

        Returns:
          A request object that you can call 'execute()' on to request the next
          page. Returns None if there are no more items in the collection.