Struct CreateEntityRecognizerInput

#[non_exhaustive]
pub struct CreateEntityRecognizerInput {
    pub recognizer_name: Option<String>,
    pub version_name: Option<String>,
    pub data_access_role_arn: Option<String>,
    pub tags: Option<Vec<Tag>>,
    pub input_data_config: Option<EntityRecognizerInputDataConfig>,
    pub client_request_token: Option<String>,
    pub language_code: Option<LanguageCode>,
    pub volume_kms_key_id: Option<String>,
    pub vpc_config: Option<VpcConfig>,
    pub model_kms_key_id: Option<String>,
    pub model_policy: Option<String>,
}

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

recognizer_name: Option<String>

The name given to the newly created recognizer. Recognizer names can be a maximum of 256 characters. Alphanumeric characters, hyphens (-) and underscores (_) are allowed. The name must be unique in the account/Region.

version_name: Option<String>

The version name given to the newly created recognizer. Version names can be a maximum of 256 characters. Alphanumeric characters, hyphens (-) and underscores (_) are allowed. The version name must be unique among all models with the same recognizer name in the account/Region.

data_access_role_arn: Option<String>

The Amazon Resource Name (ARN) of the IAM role that grants Amazon Comprehend read access to your input data.

tags: Option<Vec<Tag>>

Tags to associate with the entity recognizer. A tag is a key-value pair that adds as a metadata to a resource used by Amazon Comprehend. For example, a tag with "Sales" as the key might be added to a resource to indicate its use by the sales department.

input_data_config: Option<EntityRecognizerInputDataConfig>

Specifies the format and location of the input data. The S3 bucket containing the input data must be located in the same Region as the entity recognizer being created.

client_request_token: Option<String>

A unique identifier for the request. If you don't set the client request token, Amazon Comprehend generates one.

language_code: Option<LanguageCode>

You can specify any of the following languages: English ("en"), Spanish ("es"), French ("fr"), Italian ("it"), German ("de"), or Portuguese ("pt"). If you plan to use this entity recognizer with PDF, Word, or image input files, you must specify English as the language. All training documents must be in the same language.

volume_kms_key_id: Option<String>

ID for the Amazon Web Services Key Management Service (KMS) key that Amazon Comprehend uses to encrypt data on the storage volume attached to the ML compute instance(s) that process the analysis job. The VolumeKmsKeyId can be either of the following formats:

• KMS Key ID: "1234abcd-12ab-34cd-56ef-1234567890ab"

• Amazon Resource Name (ARN) of a KMS Key: "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

vpc_config: Option<VpcConfig>

Configuration parameters for an optional private Virtual Private Cloud (VPC) containing the resources you are using for your custom entity recognizer. For more information, see Amazon VPC.

model_kms_key_id: Option<String>

ID for the KMS key that Amazon Comprehend uses to encrypt trained custom models. The ModelKmsKeyId can be either of the following formats:

• KMS Key ID: "1234abcd-12ab-34cd-56ef-1234567890ab"

• Amazon Resource Name (ARN) of a KMS Key: "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

model_policy: Option<String>

The JSON resource-based policy to attach to your custom entity recognizer model. You can use this policy to allow another Amazon Web Services account to import your custom model.

Provide your JSON as a UTF-8 encoded string without line breaks. To provide valid JSON for your policy, enclose the attribute names and values in double quotes. If the JSON body is also enclosed in double quotes, then you must escape the double quotes that are inside the policy:

"{\"attribute\": \"value\", \"attribute\": \[\"value\"\]}"

To avoid escaping quotes, you can use single quotes to enclose the policy and double quotes to enclose the JSON names and values:

'{"attribute": "value", "attribute": \["value"\]}'

Implementations

impl CreateEntityRecognizerInput

pub fn recognizer_name(&self) -> Option<&str>

The name given to the newly created recognizer. Recognizer names can be a maximum of 256 characters. Alphanumeric characters, hyphens (-) and underscores (_) are allowed. The name must be unique in the account/Region.

pub fn version_name(&self) -> Option<&str>

The version name given to the newly created recognizer. Version names can be a maximum of 256 characters. Alphanumeric characters, hyphens (-) and underscores (_) are allowed. The version name must be unique among all models with the same recognizer name in the account/Region.

pub fn data_access_role_arn(&self) -> Option<&str>

The Amazon Resource Name (ARN) of the IAM role that grants Amazon Comprehend read access to your input data.

pub fn tags(&self) -> &[Tag]

Tags to associate with the entity recognizer. A tag is a key-value pair that adds as a metadata to a resource used by Amazon Comprehend. For example, a tag with "Sales" as the key might be added to a resource to indicate its use by the sales department.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .tags.is_none().

pub fn input_data_config(&self) -> Option<&EntityRecognizerInputDataConfig>

Specifies the format and location of the input data. The S3 bucket containing the input data must be located in the same Region as the entity recognizer being created.

pub fn client_request_token(&self) -> Option<&str>

A unique identifier for the request. If you don't set the client request token, Amazon Comprehend generates one.

pub fn language_code(&self) -> Option<&LanguageCode>

You can specify any of the following languages: English ("en"), Spanish ("es"), French ("fr"), Italian ("it"), German ("de"), or Portuguese ("pt"). If you plan to use this entity recognizer with PDF, Word, or image input files, you must specify English as the language. All training documents must be in the same language.

pub fn volume_kms_key_id(&self) -> Option<&str>

ID for the Amazon Web Services Key Management Service (KMS) key that Amazon Comprehend uses to encrypt data on the storage volume attached to the ML compute instance(s) that process the analysis job. The VolumeKmsKeyId can be either of the following formats:

• KMS Key ID: "1234abcd-12ab-34cd-56ef-1234567890ab"

• Amazon Resource Name (ARN) of a KMS Key: "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

pub fn vpc_config(&self) -> Option<&VpcConfig>

Configuration parameters for an optional private Virtual Private Cloud (VPC) containing the resources you are using for your custom entity recognizer. For more information, see Amazon VPC.

pub fn model_kms_key_id(&self) -> Option<&str>

ID for the KMS key that Amazon Comprehend uses to encrypt trained custom models. The ModelKmsKeyId can be either of the following formats:

• KMS Key ID: "1234abcd-12ab-34cd-56ef-1234567890ab"

• Amazon Resource Name (ARN) of a KMS Key: "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"

pub fn model_policy(&self) -> Option<&str>

The JSON resource-based policy to attach to your custom entity recognizer model. You can use this policy to allow another Amazon Web Services account to import your custom model.

Provide your JSON as a UTF-8 encoded string without line breaks. To provide valid JSON for your policy, enclose the attribute names and values in double quotes. If the JSON body is also enclosed in double quotes, then you must escape the double quotes that are inside the policy:

"{\"attribute\": \"value\", \"attribute\": \[\"value\"\]}"

To avoid escaping quotes, you can use single quotes to enclose the policy and double quotes to enclose the JSON names and values:

'{"attribute": "value", "attribute": \["value"\]}'

impl CreateEntityRecognizerInput

pub fn builder() -> CreateEntityRecognizerInputBuilder

Creates a new builder-style object to manufacture CreateEntityRecognizerInput.

Trait Implementations

impl Clone for CreateEntityRecognizerInput

fn clone(&self) -> CreateEntityRecognizerInput

Returns a duplicate of the value.

const fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for CreateEntityRecognizerInput

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PartialEq for CreateEntityRecognizerInput

fn eq(&self, other: &CreateEntityRecognizerInput) -> bool

Tests for self and other values to be equal, and is used by ==.

const fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for CreateEntityRecognizerInput