EvaluatorInferenceConfig

class aws_cdk.aws_bedrock_agentcore_alpha.EvaluatorInferenceConfig(*, max_tokens=None, temperature=None, top_p=None)

Bases: object

(experimental) Inference configuration for a custom LLM-as-a-Judge evaluator.

Controls how the foundation model generates evaluation responses.

Parameters:

max_tokens (Union[int, float, None]) – (experimental) The maximum number of tokens to generate in the model response. Default: - The foundation model’s default maximum token limit is used
temperature (Union[int, float, None]) – (experimental) The temperature value that controls randomness in the model’s responses. Higher values produce more diverse outputs. Range: 0.0 to 1.0. Default: - The foundation model’s default temperature is used
top_p (Union[int, float, None]) – (experimental) The top-p sampling parameter that controls the diversity of the model’s responses. Range: 0.0 to 1.0. Default: - The foundation model’s default top-p value is used

Stability:

experimental

ExampleMetadata:

fixture=_generated

Example:

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

evaluator_inference_config = bedrock_agentcore_alpha.EvaluatorInferenceConfig(
    max_tokens=123,
    temperature=123,
    top_p=123
)

Attributes

max_tokens

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

Default:

The foundation model’s default maximum token limit is used

Stability:

experimental

temperature

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

Higher values produce more diverse outputs. Range: 0.0 to 1.0.

Default:

The foundation model’s default temperature is used

Stability:

experimental

top_p

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

Range: 0.0 to 1.0.

Default:

The foundation model’s default top-p value is used

Stability:

experimental