CfnEvaluator

class aws_cdk.aws_bedrockagentcore.CfnEvaluator(scope, id, *, evaluator_config, evaluator_name, level, description=None, tags=None)

Bases: CfnResource

Resource Type definition for AWS::BedrockAgentCore::Evaluator - Creates a custom evaluator for agent quality assessment using LLM-as-a-Judge configurations.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-bedrockagentcore-evaluator.html

CloudformationResource:

AWS::BedrockAgentCore::Evaluator

ExampleMetadata:

fixture=_generated

Example:

from aws_cdk import CfnTag
# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_bedrockagentcore as bedrockagentcore

# additional_model_request_fields: Any

cfn_evaluator = bedrockagentcore.CfnEvaluator(self, "MyCfnEvaluator",
    evaluator_config=bedrockagentcore.CfnEvaluator.EvaluatorConfigProperty(
        llm_as_aJudge=bedrockagentcore.CfnEvaluator.LlmAsAJudgeEvaluatorConfigProperty(
            instructions="instructions",
            model_config=bedrockagentcore.CfnEvaluator.EvaluatorModelConfigProperty(
                bedrock_evaluator_model_config=bedrockagentcore.CfnEvaluator.BedrockEvaluatorModelConfigProperty(
                    model_id="modelId",

                    # the properties below are optional
                    additional_model_request_fields=additional_model_request_fields,
                    inference_config=bedrockagentcore.CfnEvaluator.InferenceConfigurationProperty(
                        max_tokens=123,
                        temperature=123,
                        top_p=123
                    )
                )
            ),
            rating_scale=bedrockagentcore.CfnEvaluator.RatingScaleProperty(
                categorical=[bedrockagentcore.CfnEvaluator.CategoricalScaleDefinitionProperty(
                    definition="definition",
                    label="label"
                )],
                numerical=[bedrockagentcore.CfnEvaluator.NumericalScaleDefinitionProperty(
                    definition="definition",
                    label="label",
                    value=123
                )]
            )
        )
    ),
    evaluator_name="evaluatorName",
    level="level",

    # the properties below are optional
    description="description",
    tags=[CfnTag(
        key="key",
        value="value"
    )]
)

Create a new AWS::BedrockAgentCore::Evaluator.

Parameters:

• scope (Construct) – Scope in which this resource is defined.

• id (str) – Construct identifier for this resource (unique in its scope).

• evaluator_config (Union[IResolvable, EvaluatorConfigProperty, Dict[str, Any]]) – The configuration that defines how an evaluator assesses agent performance.

• evaluator_name (str) – The name of the evaluator. Must be unique within your account.

• level (str)

• description (Optional[str]) – The description of the evaluator.

• tags (Optional[Sequence[Union[CfnTag, Dict[str, Any]]]]) – A list of tags to assign to the evaluator.

Methods

add_deletion_override(path)

Syntactic sugar for addOverride(path, undefined).

Parameters:

path (str) – The path of the value to delete.

Return type:

None

add_dependency(target)

Indicates that this resource depends on another resource and cannot be provisioned unless the other resource has been successfully provisioned.

This can be used for resources across stacks (or nested stack) boundaries and the dependency will automatically be transferred to the relevant scope.

Parameters:

target (CfnResource)

Return type:

None

add_depends_on(target)

(deprecated) Indicates that this resource depends on another resource and cannot be provisioned unless the other resource has been successfully provisioned.

Parameters:

target (CfnResource)

Deprecated:

use addDependency

Stability:

deprecated

Return type:

None

add_metadata(key, value)

Add a value to the CloudFormation Resource Metadata.

Parameters:

• key (str)

• value (Any)

See:

Return type:

None

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/metadata-section-structure.html

Note that this is a different set of metadata from CDK node metadata; this metadata ends up in the stack template under the resource, whereas CDK node metadata ends up in the Cloud Assembly.

add_override(path, value)

Adds an override to the synthesized CloudFormation resource.

To add a property override, either use addPropertyOverride or prefix path with “Properties.” (i.e. Properties.TopicName).

If the override is nested, separate each nested level using a dot (.) in the path parameter. If there is an array as part of the nesting, specify the index in the path.

To include a literal . in the property name, prefix with a \. In most programming languages you will need to write this as "\\." because the \ itself will need to be escaped.

For example:

cfn_resource.add_override("Properties.GlobalSecondaryIndexes.0.Projection.NonKeyAttributes", ["myattribute"])
cfn_resource.add_override("Properties.GlobalSecondaryIndexes.1.ProjectionType", "INCLUDE")

would add the overrides Example:

"Properties": {
  "GlobalSecondaryIndexes": [
    {
      "Projection": {
        "NonKeyAttributes": [ "myattribute" ]
        ...
      }
      ...
    },
    {
      "ProjectionType": "INCLUDE"
      ...
    },
  ]
  ...
}

The value argument to addOverride will not be processed or translated in any way. Pass raw JSON values in here with the correct capitalization for CloudFormation. If you pass CDK classes or structs, they will be rendered with lowercased key names, and CloudFormation will reject the template.

Parameters:

• path (str) –

  • The path of the property, you can use dot notation to override values in complex types. Any intermediate keys will be created as needed.

• value (Any) –

  • The value. Could be primitive or complex.

Return type:

None

add_property_deletion_override(property_path)

Adds an override that deletes the value of a property from the resource definition.

Parameters:

property_path (str) – The path to the property.

Return type:

None

add_property_override(property_path, value)

Adds an override to a resource property.

Syntactic sugar for addOverride("Properties.<...>", value).

Parameters:

• property_path (str) – The path of the property.

• value (Any) – The value.

Return type:

None

apply_removal_policy(policy=None, *, apply_to_update_replace_policy=None, default=None)

Sets the deletion policy of the resource based on the removal policy specified.

The Removal Policy controls what happens to this resource when it stops being managed by CloudFormation, either because you’ve removed it from the CDK application or because you’ve made a change that requires the resource to be replaced.

The resource can be deleted (RemovalPolicy.DESTROY), or left in your AWS account for data recovery and cleanup later (RemovalPolicy.RETAIN). In some cases, a snapshot can be taken of the resource prior to deletion (RemovalPolicy.SNAPSHOT). A list of resources that support this policy can be found in the following link:

Parameters:

• policy (Optional[RemovalPolicy])

• apply_to_update_replace_policy (Optional[bool]) – Apply the same deletion policy to the resource’s “UpdateReplacePolicy”. Default: true

• default (Optional[RemovalPolicy]) – The default policy to apply in case the removal policy is not defined. Default: - Default value is resource specific. To determine the default value for a resource, please consult that specific resource’s documentation.

See:

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-attribute-deletionpolicy.html#aws-attribute-deletionpolicy-options

Return type:

None

get_att(attribute_name, type_hint=None)

Returns a token for an runtime attribute of this resource.

Ideally, use generated attribute accessors (e.g. resource.arn), but this can be used for future compatibility in case there is no generated attribute.

Parameters:

• attribute_name (str) – The name of the attribute.

• type_hint (Optional[ResolutionTypeHint])

Return type:

Reference

get_metadata(key)

Retrieve a value value from the CloudFormation Resource Metadata.

Parameters:

key (str)

See:

Return type:

Any

https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/metadata-section-structure.html

Note that this is a different set of metadata from CDK node metadata; this metadata ends up in the stack template under the resource, whereas CDK node metadata ends up in the Cloud Assembly.

inspect(inspector)

Examines the CloudFormation resource and discloses attributes.

Parameters:

inspector (TreeInspector) – tree inspector to collect and process attributes.

Return type:

None

obtain_dependencies()

Retrieves an array of resources this resource depends on.

This assembles dependencies on resources across stacks (including nested stacks) automatically.

Return type:

List[Union[Stack, CfnResource]]

obtain_resource_dependencies()

Get a shallow copy of dependencies between this resource and other resources in the same stack.

Return type:

List[CfnResource]

override_logical_id(new_logical_id)

Overrides the auto-generated logical ID with a specific ID.

Parameters:

new_logical_id (str) – The new logical ID to use for this stack element.

Return type:

None

remove_dependency(target)

Indicates that this resource no longer depends on another resource.

This can be used for resources across stacks (including nested stacks) and the dependency will automatically be removed from the relevant scope.

Parameters:

target (CfnResource)

Return type:

None

replace_dependency(target, new_target)

Replaces one dependency with another.

Parameters:

• target (CfnResource) – The dependency to replace.

• new_target (CfnResource) – The new dependency to add.

Return type:

None

to_string()

Returns a string representation of this construct.

Return type:

str

Returns:

a string representation of this resource

with_(*mixins)

Applies one or more mixins to this construct.

Mixins are applied in order. The list of constructs is captured at the start of the call, so constructs added by a mixin will not be visited. Use multiple with() calls if subsequent mixins should apply to added constructs.

Parameters:

mixins (IMixin)

Return type:

IConstruct

Attributes

CFN_RESOURCE_TYPE_NAME = 'AWS::BedrockAgentCore::Evaluator'

attr_created_at

The timestamp when the evaluator was created.

CloudformationAttribute:

CreatedAt

attr_evaluator_arn

The Amazon Resource Name (ARN) of the evaluator.

CloudformationAttribute:

EvaluatorArn

attr_evaluator_id

The unique identifier of the evaluator.

CloudformationAttribute:

EvaluatorId

attr_status

Status

Type:

cloudformationAttribute

attr_updated_at

The timestamp when the evaluator was last updated.

CloudformationAttribute:

UpdatedAt

cdk_tag_manager

Tag Manager which manages the tags for this resource.

cfn_options

Options for this resource, such as condition, update policy etc.

cfn_resource_type

AWS resource type.

creation_stack

return:

the stack trace of the point where this Resource was created from, sourced from the +metadata+ entry typed +aws:cdk:logicalId+, and with the bottom-most node +internal+ entries filtered.

description

The description of the evaluator.

env

evaluator_config

The configuration that defines how an evaluator assesses agent performance.

evaluator_name

The name of the evaluator.

evaluator_ref

A reference to a Evaluator resource.

level

logical_id

The logical ID for this CloudFormation stack element.

The logical ID of the element is calculated from the path of the resource node in the construct tree.

To override this value, use overrideLogicalId(newLogicalId).

Returns:

the logical ID as a stringified token. This value will only get resolved during synthesis.

node

The tree node.

ref

Return a string that will be resolved to a CloudFormation { Ref } for this element.

If, by any chance, the intrinsic reference of a resource is not a string, you could coerce it to an IResolvable through Lazy.any({ produce: resource.ref }).

stack

The stack in which this element is defined.

CfnElements must be defined within a stack scope (directly or indirectly).

tags

A list of tags to assign to the evaluator.

Static Methods

classmethod arn_for_evaluator(resource)

Parameters:

resource (IEvaluatorRef)

Return type:

str

classmethod is_cfn_element(x)

Returns true if a construct is a stack element (i.e. part of the synthesized cloudformation template).

Uses duck-typing instead of instanceof to allow stack elements from different versions of this library to be included in the same stack.

Parameters:

x (Any)

Return type:

bool

Returns:

The construct as a stack element or undefined if it is not a stack element.

classmethod is_cfn_evaluator(x)

Checks whether the given object is a CfnEvaluator.

Parameters:

x (Any)

Return type:

bool

classmethod is_cfn_resource(x)

Check whether the given object is a CfnResource.

Parameters:

x (Any)

Return type:

bool

classmethod is_construct(x)

Checks if x is a construct.

Use this method instead of instanceof to properly detect Construct instances, even when the construct library is symlinked.

Explanation: in JavaScript, multiple copies of the constructs library on disk are seen as independent, completely different libraries. As a consequence, the class Construct in each copy of the constructs library is seen as a different class, and an instance of one class will not test as instanceof the other class. npm install will not create installations like this, but users may manually symlink construct libraries together or use a monorepo tool: in those cases, multiple copies of the constructs library can be accidentally installed, and instanceof will behave unpredictably. It is safest to avoid using instanceof, and using this type-testing method instead.

Parameters:

x (Any) – Any object.

Return type:

bool

Returns:

true if x is an object created from a class which extends Construct.

BedrockEvaluatorModelConfigProperty

class CfnEvaluator.BedrockEvaluatorModelConfigProperty(*, model_id, additional_model_request_fields=None, inference_config=None)

Bases: object

The configuration for using Amazon Bedrock models in evaluator assessments.

Parameters:

• model_id (str) – The identifier of the Amazon Bedrock model to use for evaluation.

• additional_model_request_fields (Any) – Additional model-specific request fields.

• inference_config (Union[IResolvable, InferenceConfigurationProperty, Dict[str, Any], None]) – The inference configuration parameters that control model behavior during evaluation.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-bedrockagentcore-evaluator-bedrockevaluatormodelconfig.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_bedrockagentcore as bedrockagentcore

# additional_model_request_fields: Any

bedrock_evaluator_model_config_property = bedrockagentcore.CfnEvaluator.BedrockEvaluatorModelConfigProperty(
    model_id="modelId",

    # the properties below are optional
    additional_model_request_fields=additional_model_request_fields,
    inference_config=bedrockagentcore.CfnEvaluator.InferenceConfigurationProperty(
        max_tokens=123,
        temperature=123,
        top_p=123
    )
)

Attributes

additional_model_request_fields

Additional model-specific request fields.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-bedrockagentcore-evaluator-bedrockevaluatormodelconfig.html#cfn-bedrockagentcore-evaluator-bedrockevaluatormodelconfig-additionalmodelrequestfields

inference_config

The inference configuration parameters that control model behavior during evaluation.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-bedrockagentcore-evaluator-bedrockevaluatormodelconfig.html#cfn-bedrockagentcore-evaluator-bedrockevaluatormodelconfig-inferenceconfig

model_id

The identifier of the Amazon Bedrock model to use for evaluation.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-bedrockagentcore-evaluator-bedrockevaluatormodelconfig.html#cfn-bedrockagentcore-evaluator-bedrockevaluatormodelconfig-modelid

CategoricalScaleDefinitionProperty

class CfnEvaluator.CategoricalScaleDefinitionProperty(*, definition, label)

Bases: object

A categorical rating scale option.

Parameters:

• definition (str) – The description that explains what this categorical rating represents.

• label (str) – The label of this categorical rating option.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-bedrockagentcore-evaluator-categoricalscaledefinition.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_bedrockagentcore as bedrockagentcore

categorical_scale_definition_property = bedrockagentcore.CfnEvaluator.CategoricalScaleDefinitionProperty(
    definition="definition",
    label="label"
)

Attributes

definition

The description that explains what this categorical rating represents.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-bedrockagentcore-evaluator-categoricalscaledefinition.html#cfn-bedrockagentcore-evaluator-categoricalscaledefinition-definition

label

The label of this categorical rating option.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-bedrockagentcore-evaluator-categoricalscaledefinition.html#cfn-bedrockagentcore-evaluator-categoricalscaledefinition-label

EvaluatorConfigProperty

class CfnEvaluator.EvaluatorConfigProperty(*, llm_as_a_judge)

Bases: object

The configuration that defines how an evaluator assesses agent performance.

Parameters:

llm_as_a_judge (Union[IResolvable, LlmAsAJudgeEvaluatorConfigProperty, Dict[str, Any]]) – The configuration for LLM-as-a-Judge evaluation.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-bedrockagentcore-evaluator-evaluatorconfig.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_bedrockagentcore as bedrockagentcore

# additional_model_request_fields: Any

evaluator_config_property = bedrockagentcore.CfnEvaluator.EvaluatorConfigProperty(
    llm_as_aJudge=bedrockagentcore.CfnEvaluator.LlmAsAJudgeEvaluatorConfigProperty(
        instructions="instructions",
        model_config=bedrockagentcore.CfnEvaluator.EvaluatorModelConfigProperty(
            bedrock_evaluator_model_config=bedrockagentcore.CfnEvaluator.BedrockEvaluatorModelConfigProperty(
                model_id="modelId",

                # the properties below are optional
                additional_model_request_fields=additional_model_request_fields,
                inference_config=bedrockagentcore.CfnEvaluator.InferenceConfigurationProperty(
                    max_tokens=123,
                    temperature=123,
                    top_p=123
                )
            )
        ),
        rating_scale=bedrockagentcore.CfnEvaluator.RatingScaleProperty(
            categorical=[bedrockagentcore.CfnEvaluator.CategoricalScaleDefinitionProperty(
                definition="definition",
                label="label"
            )],
            numerical=[bedrockagentcore.CfnEvaluator.NumericalScaleDefinitionProperty(
                definition="definition",
                label="label",
                value=123
            )]
        )
    )
)

Attributes

llm_as_a_judge

The configuration for LLM-as-a-Judge evaluation.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-bedrockagentcore-evaluator-evaluatorconfig.html#cfn-bedrockagentcore-evaluator-evaluatorconfig-llmasajudge

EvaluatorModelConfigProperty

class CfnEvaluator.EvaluatorModelConfigProperty(*, bedrock_evaluator_model_config)

Bases: object

The model configuration that specifies which foundation model to use for evaluation.

Parameters:

bedrock_evaluator_model_config (Union[IResolvable, BedrockEvaluatorModelConfigProperty, Dict[str, Any]]) – The configuration for using Amazon Bedrock models in evaluator assessments.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-bedrockagentcore-evaluator-evaluatormodelconfig.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_bedrockagentcore as bedrockagentcore

# additional_model_request_fields: Any

evaluator_model_config_property = bedrockagentcore.CfnEvaluator.EvaluatorModelConfigProperty(
    bedrock_evaluator_model_config=bedrockagentcore.CfnEvaluator.BedrockEvaluatorModelConfigProperty(
        model_id="modelId",

        # the properties below are optional
        additional_model_request_fields=additional_model_request_fields,
        inference_config=bedrockagentcore.CfnEvaluator.InferenceConfigurationProperty(
            max_tokens=123,
            temperature=123,
            top_p=123
        )
    )
)

Attributes

bedrock_evaluator_model_config

The configuration for using Amazon Bedrock models in evaluator assessments.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-bedrockagentcore-evaluator-evaluatormodelconfig.html#cfn-bedrockagentcore-evaluator-evaluatormodelconfig-bedrockevaluatormodelconfig

InferenceConfigurationProperty

class CfnEvaluator.InferenceConfigurationProperty(*, max_tokens=None, temperature=None, top_p=None)

Bases: object

The inference configuration parameters that control model behavior during evaluation.

Parameters:

• max_tokens (Union[int, float, None]) – The maximum number of tokens to generate in the model response.

• temperature (Union[int, float, None]) – The temperature value that controls randomness in the model’s responses.

• top_p (Union[int, float, None]) – The top-p sampling parameter that controls the diversity of the model’s responses.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-bedrockagentcore-evaluator-inferenceconfiguration.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_bedrockagentcore as bedrockagentcore

inference_configuration_property = bedrockagentcore.CfnEvaluator.InferenceConfigurationProperty(
    max_tokens=123,
    temperature=123,
    top_p=123
)

Attributes

max_tokens

The maximum number of tokens to generate in the model response.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-bedrockagentcore-evaluator-inferenceconfiguration.html#cfn-bedrockagentcore-evaluator-inferenceconfiguration-maxtokens

temperature

The temperature value that controls randomness in the model’s responses.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-bedrockagentcore-evaluator-inferenceconfiguration.html#cfn-bedrockagentcore-evaluator-inferenceconfiguration-temperature

top_p

The top-p sampling parameter that controls the diversity of the model’s responses.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-bedrockagentcore-evaluator-inferenceconfiguration.html#cfn-bedrockagentcore-evaluator-inferenceconfiguration-topp

LlmAsAJudgeEvaluatorConfigProperty

class CfnEvaluator.LlmAsAJudgeEvaluatorConfigProperty(*, instructions, model_config, rating_scale)

Bases: object

The configuration for LLM-as-a-Judge evaluation.

Parameters:

• instructions (str) – The evaluation instructions that guide the language model in assessing agent performance.

• model_config (Union[IResolvable, EvaluatorModelConfigProperty, Dict[str, Any]]) – The model configuration that specifies which foundation model to use for evaluation.

• rating_scale (Union[IResolvable, RatingScaleProperty, Dict[str, Any]]) – The rating scale that defines how evaluators should score agent performance.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-bedrockagentcore-evaluator-llmasajudgeevaluatorconfig.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_bedrockagentcore as bedrockagentcore

# additional_model_request_fields: Any

llm_as_aJudge_evaluator_config_property = bedrockagentcore.CfnEvaluator.LlmAsAJudgeEvaluatorConfigProperty(
    instructions="instructions",
    model_config=bedrockagentcore.CfnEvaluator.EvaluatorModelConfigProperty(
        bedrock_evaluator_model_config=bedrockagentcore.CfnEvaluator.BedrockEvaluatorModelConfigProperty(
            model_id="modelId",

            # the properties below are optional
            additional_model_request_fields=additional_model_request_fields,
            inference_config=bedrockagentcore.CfnEvaluator.InferenceConfigurationProperty(
                max_tokens=123,
                temperature=123,
                top_p=123
            )
        )
    ),
    rating_scale=bedrockagentcore.CfnEvaluator.RatingScaleProperty(
        categorical=[bedrockagentcore.CfnEvaluator.CategoricalScaleDefinitionProperty(
            definition="definition",
            label="label"
        )],
        numerical=[bedrockagentcore.CfnEvaluator.NumericalScaleDefinitionProperty(
            definition="definition",
            label="label",
            value=123
        )]
    )
)

Attributes

instructions

The evaluation instructions that guide the language model in assessing agent performance.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-bedrockagentcore-evaluator-llmasajudgeevaluatorconfig.html#cfn-bedrockagentcore-evaluator-llmasajudgeevaluatorconfig-instructions

model_config

The model configuration that specifies which foundation model to use for evaluation.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-bedrockagentcore-evaluator-llmasajudgeevaluatorconfig.html#cfn-bedrockagentcore-evaluator-llmasajudgeevaluatorconfig-modelconfig

rating_scale

The rating scale that defines how evaluators should score agent performance.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-bedrockagentcore-evaluator-llmasajudgeevaluatorconfig.html#cfn-bedrockagentcore-evaluator-llmasajudgeevaluatorconfig-ratingscale

NumericalScaleDefinitionProperty

class CfnEvaluator.NumericalScaleDefinitionProperty(*, definition, label, value)

Bases: object

A numerical rating scale option.

Parameters:

• definition (str) – The description that explains what this numerical rating represents.

• label (str) – The label that describes this numerical rating option.

• value (Union[int, float]) – The numerical value for this rating scale option.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-bedrockagentcore-evaluator-numericalscaledefinition.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_bedrockagentcore as bedrockagentcore

numerical_scale_definition_property = bedrockagentcore.CfnEvaluator.NumericalScaleDefinitionProperty(
    definition="definition",
    label="label",
    value=123
)

Attributes

definition

The description that explains what this numerical rating represents.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-bedrockagentcore-evaluator-numericalscaledefinition.html#cfn-bedrockagentcore-evaluator-numericalscaledefinition-definition

label

The label that describes this numerical rating option.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-bedrockagentcore-evaluator-numericalscaledefinition.html#cfn-bedrockagentcore-evaluator-numericalscaledefinition-label

value

The numerical value for this rating scale option.

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-bedrockagentcore-evaluator-numericalscaledefinition.html#cfn-bedrockagentcore-evaluator-numericalscaledefinition-value

RatingScaleProperty

class CfnEvaluator.RatingScaleProperty(*, categorical=None, numerical=None)

Bases: object

The rating scale that defines how evaluators should score agent performance.

Parameters:

• categorical (Union[IResolvable, Sequence[Union[IResolvable, CategoricalScaleDefinitionProperty, Dict[str, Any]]], None])

• numerical (Union[IResolvable, Sequence[Union[IResolvable, NumericalScaleDefinitionProperty, Dict[str, Any]]], None])

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-bedrockagentcore-evaluator-ratingscale.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_bedrockagentcore as bedrockagentcore

rating_scale_property = bedrockagentcore.CfnEvaluator.RatingScaleProperty(
    categorical=[bedrockagentcore.CfnEvaluator.CategoricalScaleDefinitionProperty(
        definition="definition",
        label="label"
    )],
    numerical=[bedrockagentcore.CfnEvaluator.NumericalScaleDefinitionProperty(
        definition="definition",
        label="label",
        value=123
    )]
)

Attributes

categorical

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-bedrockagentcore-evaluator-ratingscale.html#cfn-bedrockagentcore-evaluator-ratingscale-categorical

Type:

see

numerical

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-bedrockagentcore-evaluator-ratingscale.html#cfn-bedrockagentcore-evaluator-ratingscale-numerical

Type:

see