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// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
#[allow(missing_docs)] // documentation missing in model
#[non_exhaustive]
#[derive(::std::clone::Clone, ::std::cmp::PartialEq)]
pub struct EvaluateInput {
/// <p>The unique identifier of the evaluator to use for scoring. Can be a built-in evaluator (e.g., <code>Builtin.Helpfulness</code>, <code>Builtin.Correctness</code>) or a custom evaluator Id created through the control plane API.</p>
pub evaluator_id: ::std::option::Option<::std::string::String>,
/// <p>The input data containing agent session spans to be evaluated. Includes a list of spans in OpenTelemetry format from supported frameworks like Strands (AgentCore Runtime) or LangGraph with OpenInference instrumentation.</p>
pub evaluation_input: ::std::option::Option<crate::types::EvaluationInput>,
/// <p>The specific trace or span IDs to evaluate within the provided input. Allows targeting evaluation at different levels: individual tool calls, single request-response interactions (traces), or entire conversation sessions.</p>
pub evaluation_target: ::std::option::Option<crate::types::EvaluationTarget>,
/// <p>Ground truth data to compare against agent responses during evaluation. Allows to provide expected responses, assertions, and expected tool trajectories at different evaluation levels. Session-level reference inputs apply to the entire conversation, while trace-level reference inputs target specific request-response interactions identified by trace ID.</p>
pub evaluation_reference_inputs: ::std::option::Option<::std::vec::Vec<crate::types::EvaluationReferenceInput>>,
}
impl EvaluateInput {
/// <p>The unique identifier of the evaluator to use for scoring. Can be a built-in evaluator (e.g., <code>Builtin.Helpfulness</code>, <code>Builtin.Correctness</code>) or a custom evaluator Id created through the control plane API.</p>
pub fn evaluator_id(&self) -> ::std::option::Option<&str> {
self.evaluator_id.as_deref()
}
/// <p>The input data containing agent session spans to be evaluated. Includes a list of spans in OpenTelemetry format from supported frameworks like Strands (AgentCore Runtime) or LangGraph with OpenInference instrumentation.</p>
pub fn evaluation_input(&self) -> ::std::option::Option<&crate::types::EvaluationInput> {
self.evaluation_input.as_ref()
}
/// <p>The specific trace or span IDs to evaluate within the provided input. Allows targeting evaluation at different levels: individual tool calls, single request-response interactions (traces), or entire conversation sessions.</p>
pub fn evaluation_target(&self) -> ::std::option::Option<&crate::types::EvaluationTarget> {
self.evaluation_target.as_ref()
}
/// <p>Ground truth data to compare against agent responses during evaluation. Allows to provide expected responses, assertions, and expected tool trajectories at different evaluation levels. Session-level reference inputs apply to the entire conversation, while trace-level reference inputs target specific request-response interactions identified by trace ID.</p>
///
/// If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use `.evaluation_reference_inputs.is_none()`.
pub fn evaluation_reference_inputs(&self) -> &[crate::types::EvaluationReferenceInput] {
self.evaluation_reference_inputs.as_deref().unwrap_or_default()
}
}
impl ::std::fmt::Debug for EvaluateInput {
fn fmt(&self, f: &mut ::std::fmt::Formatter<'_>) -> ::std::fmt::Result {
let mut formatter = f.debug_struct("EvaluateInput");
formatter.field("evaluator_id", &self.evaluator_id);
formatter.field("evaluation_input", &self.evaluation_input);
formatter.field("evaluation_target", &self.evaluation_target);
formatter.field("evaluation_reference_inputs", &"*** Sensitive Data Redacted ***");
formatter.finish()
}
}
impl EvaluateInput {
/// Creates a new builder-style object to manufacture [`EvaluateInput`](crate::operation::evaluate::EvaluateInput).
pub fn builder() -> crate::operation::evaluate::builders::EvaluateInputBuilder {
crate::operation::evaluate::builders::EvaluateInputBuilder::default()
}
}
/// A builder for [`EvaluateInput`](crate::operation::evaluate::EvaluateInput).
#[derive(::std::clone::Clone, ::std::cmp::PartialEq, ::std::default::Default)]
#[non_exhaustive]
pub struct EvaluateInputBuilder {
pub(crate) evaluator_id: ::std::option::Option<::std::string::String>,
pub(crate) evaluation_input: ::std::option::Option<crate::types::EvaluationInput>,
pub(crate) evaluation_target: ::std::option::Option<crate::types::EvaluationTarget>,
pub(crate) evaluation_reference_inputs: ::std::option::Option<::std::vec::Vec<crate::types::EvaluationReferenceInput>>,
}
impl EvaluateInputBuilder {
/// <p>The unique identifier of the evaluator to use for scoring. Can be a built-in evaluator (e.g., <code>Builtin.Helpfulness</code>, <code>Builtin.Correctness</code>) or a custom evaluator Id created through the control plane API.</p>
/// This field is required.
pub fn evaluator_id(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.evaluator_id = ::std::option::Option::Some(input.into());
self
}
/// <p>The unique identifier of the evaluator to use for scoring. Can be a built-in evaluator (e.g., <code>Builtin.Helpfulness</code>, <code>Builtin.Correctness</code>) or a custom evaluator Id created through the control plane API.</p>
pub fn set_evaluator_id(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.evaluator_id = input;
self
}
/// <p>The unique identifier of the evaluator to use for scoring. Can be a built-in evaluator (e.g., <code>Builtin.Helpfulness</code>, <code>Builtin.Correctness</code>) or a custom evaluator Id created through the control plane API.</p>
pub fn get_evaluator_id(&self) -> &::std::option::Option<::std::string::String> {
&self.evaluator_id
}
/// <p>The input data containing agent session spans to be evaluated. Includes a list of spans in OpenTelemetry format from supported frameworks like Strands (AgentCore Runtime) or LangGraph with OpenInference instrumentation.</p>
/// This field is required.
pub fn evaluation_input(mut self, input: crate::types::EvaluationInput) -> Self {
self.evaluation_input = ::std::option::Option::Some(input);
self
}
/// <p>The input data containing agent session spans to be evaluated. Includes a list of spans in OpenTelemetry format from supported frameworks like Strands (AgentCore Runtime) or LangGraph with OpenInference instrumentation.</p>
pub fn set_evaluation_input(mut self, input: ::std::option::Option<crate::types::EvaluationInput>) -> Self {
self.evaluation_input = input;
self
}
/// <p>The input data containing agent session spans to be evaluated. Includes a list of spans in OpenTelemetry format from supported frameworks like Strands (AgentCore Runtime) or LangGraph with OpenInference instrumentation.</p>
pub fn get_evaluation_input(&self) -> &::std::option::Option<crate::types::EvaluationInput> {
&self.evaluation_input
}
/// <p>The specific trace or span IDs to evaluate within the provided input. Allows targeting evaluation at different levels: individual tool calls, single request-response interactions (traces), or entire conversation sessions.</p>
pub fn evaluation_target(mut self, input: crate::types::EvaluationTarget) -> Self {
self.evaluation_target = ::std::option::Option::Some(input);
self
}
/// <p>The specific trace or span IDs to evaluate within the provided input. Allows targeting evaluation at different levels: individual tool calls, single request-response interactions (traces), or entire conversation sessions.</p>
pub fn set_evaluation_target(mut self, input: ::std::option::Option<crate::types::EvaluationTarget>) -> Self {
self.evaluation_target = input;
self
}
/// <p>The specific trace or span IDs to evaluate within the provided input. Allows targeting evaluation at different levels: individual tool calls, single request-response interactions (traces), or entire conversation sessions.</p>
pub fn get_evaluation_target(&self) -> &::std::option::Option<crate::types::EvaluationTarget> {
&self.evaluation_target
}
/// Appends an item to `evaluation_reference_inputs`.
///
/// To override the contents of this collection use [`set_evaluation_reference_inputs`](Self::set_evaluation_reference_inputs).
///
/// <p>Ground truth data to compare against agent responses during evaluation. Allows to provide expected responses, assertions, and expected tool trajectories at different evaluation levels. Session-level reference inputs apply to the entire conversation, while trace-level reference inputs target specific request-response interactions identified by trace ID.</p>
pub fn evaluation_reference_inputs(mut self, input: crate::types::EvaluationReferenceInput) -> Self {
let mut v = self.evaluation_reference_inputs.unwrap_or_default();
v.push(input);
self.evaluation_reference_inputs = ::std::option::Option::Some(v);
self
}
/// <p>Ground truth data to compare against agent responses during evaluation. Allows to provide expected responses, assertions, and expected tool trajectories at different evaluation levels. Session-level reference inputs apply to the entire conversation, while trace-level reference inputs target specific request-response interactions identified by trace ID.</p>
pub fn set_evaluation_reference_inputs(mut self, input: ::std::option::Option<::std::vec::Vec<crate::types::EvaluationReferenceInput>>) -> Self {
self.evaluation_reference_inputs = input;
self
}
/// <p>Ground truth data to compare against agent responses during evaluation. Allows to provide expected responses, assertions, and expected tool trajectories at different evaluation levels. Session-level reference inputs apply to the entire conversation, while trace-level reference inputs target specific request-response interactions identified by trace ID.</p>
pub fn get_evaluation_reference_inputs(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::EvaluationReferenceInput>> {
&self.evaluation_reference_inputs
}
/// Consumes the builder and constructs a [`EvaluateInput`](crate::operation::evaluate::EvaluateInput).
pub fn build(self) -> ::std::result::Result<crate::operation::evaluate::EvaluateInput, ::aws_smithy_types::error::operation::BuildError> {
::std::result::Result::Ok(crate::operation::evaluate::EvaluateInput {
evaluator_id: self.evaluator_id,
evaluation_input: self.evaluation_input,
evaluation_target: self.evaluation_target,
evaluation_reference_inputs: self.evaluation_reference_inputs,
})
}
}
impl ::std::fmt::Debug for EvaluateInputBuilder {
fn fmt(&self, f: &mut ::std::fmt::Formatter<'_>) -> ::std::fmt::Result {
let mut formatter = f.debug_struct("EvaluateInputBuilder");
formatter.field("evaluator_id", &self.evaluator_id);
formatter.field("evaluation_input", &self.evaluation_input);
formatter.field("evaluation_target", &self.evaluation_target);
formatter.field("evaluation_reference_inputs", &"*** Sensitive Data Redacted ***");
formatter.finish()
}
}