puttrainedmodel

type NewPutTrainedModel func(modelid string) *PutTrainedModel

type PutTrainedModel struct {
	// contains filtered or unexported fields
}

type Request struct {

	// CompressedDefinition The compressed (GZipped and Base64 encoded) inference definition of the
	// model. If compressed_definition is specified, then definition cannot be
	// specified.
	CompressedDefinition *string `json:"compressed_definition,omitempty"`
	// Definition The inference definition for the model. If definition is specified, then
	// compressed_definition cannot be specified.
	Definition *types.Definition `json:"definition,omitempty"`
	// Description A human-readable description of the inference trained model.
	Description *string `json:"description,omitempty"`
	// InferenceConfig The default configuration for inference. This can be either a regression
	// or classification configuration. It must match the underlying
	// definition.trained_model's target_type. For pre-packaged models such as
	// ELSER the config is not required.
	InferenceConfig *types.InferenceConfigCreateContainer `json:"inference_config,omitempty"`
	// Input The input field names for the model definition.
	Input *types.Input `json:"input,omitempty"`
	// Metadata An object map that contains metadata about the model.
	Metadata json.RawMessage `json:"metadata,omitempty"`
	// ModelSizeBytes The estimated memory usage in bytes to keep the trained model in memory.
	// This property is supported only if defer_definition_decompression is true
	// or the model definition is not supplied.
	ModelSizeBytes *int64 `json:"model_size_bytes,omitempty"`
	// ModelType The model type.
	ModelType *trainedmodeltype.TrainedModelType `json:"model_type,omitempty"`
	// PlatformArchitecture The platform architecture (if applicable) of the trained mode. If the model
	// only works on one platform, because it is heavily optimized for a particular
	// processor architecture and OS combination, then this field specifies which.
	// The format of the string must match the platform identifiers used by
	// Elasticsearch,
	// so one of, `linux-x86_64`, `linux-aarch64`, `darwin-x86_64`,
	// `darwin-aarch64`,
	// or `windows-x86_64`. For portable models (those that work independent of
	// processor
	// architecture or OS features), leave this field unset.
	PlatformArchitecture *string `json:"platform_architecture,omitempty"`
	// PrefixStrings Optional prefix strings applied at inference
	PrefixStrings *types.TrainedModelPrefixStrings `json:"prefix_strings,omitempty"`
	// Tags An array of tags to organize the model.
	Tags []string `json:"tags,omitempty"`
}

type Response struct {
	CompressedDefinition *string `json:"compressed_definition,omitempty"`
	// CreateTime The time when the trained model was created.
	CreateTime types.DateTime `json:"create_time,omitempty"`
	// CreatedBy Information on the creator of the trained model.
	CreatedBy *string `json:"created_by,omitempty"`
	// DefaultFieldMap Any field map described in the inference configuration takes precedence.
	DefaultFieldMap map[string]string `json:"default_field_map,omitempty"`
	// Description The free-text description of the trained model.
	Description *string `json:"description,omitempty"`
	// EstimatedHeapMemoryUsageBytes The estimated heap usage in bytes to keep the trained model in memory.
	EstimatedHeapMemoryUsageBytes *int `json:"estimated_heap_memory_usage_bytes,omitempty"`
	// EstimatedOperations The estimated number of operations to use the trained model.
	EstimatedOperations *int `json:"estimated_operations,omitempty"`
	// FullyDefined True if the full model definition is present.
	FullyDefined *bool `json:"fully_defined,omitempty"`
	// InferenceConfig The default configuration for inference. This can be either a regression,
	// classification, or one of the many NLP focused configurations. It must match
	// the underlying definition.trained_model's target_type. For pre-packaged
	// models such as ELSER the config is not required.
	InferenceConfig *types.InferenceConfigCreateContainer `json:"inference_config,omitempty"`
	// Input The input field names for the model definition.
	Input types.TrainedModelConfigInput `json:"input"`
	// LicenseLevel The license level of the trained model.
	LicenseLevel *string                     `json:"license_level,omitempty"`
	Location     *types.TrainedModelLocation `json:"location,omitempty"`
	// Metadata An object containing metadata about the trained model. For example, models
	// created by data frame analytics contain analysis_config and input objects.
	Metadata *types.TrainedModelConfigMetadata `json:"metadata,omitempty"`
	// ModelId Identifier for the trained model.
	ModelId        string         `json:"model_id"`
	ModelSizeBytes types.ByteSize `json:"model_size_bytes,omitempty"`
	// ModelType The model type
	ModelType     *trainedmodeltype.TrainedModelType `json:"model_type,omitempty"`
	PrefixStrings *types.TrainedModelPrefixStrings   `json:"prefix_strings,omitempty"`
	// Tags A comma delimited string of tags. A trained model can have many tags, or
	// none.
	Tags []string `json:"tags"`
	// Version The Elasticsearch version number in which the trained model was created.
	Version *string `json:"version,omitempty"`
}