// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::converse::_converse_input::ConverseInputBuilder;
pub use crate::operation::converse::_converse_output::ConverseOutputBuilder;
impl crate::operation::converse::builders::ConverseInputBuilder {
/// Sends a request with this input using the given client.
pub async fn send_with(
self,
client: &crate::Client,
) -> ::std::result::Result<
crate::operation::converse::ConverseOutput,
::aws_smithy_runtime_api::client::result::SdkError<
crate::operation::converse::ConverseError,
::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
>,
> {
let mut fluent_builder = client.converse();
fluent_builder.inner = self;
fluent_builder.send().await
}
}
/// Fluent builder constructing a request to `Converse`.
///
/// <p>Sends messages to the specified Amazon Bedrock model. <code>Converse</code> provides a consistent interface that works with all models that support messages. This allows you to write code once and use it with different models. If a model has unique inference parameters, you can also pass those unique parameters to the model.</p>
/// <p>Amazon Bedrock doesn't store any text, images, or documents that you provide as content. The data is only used to generate the response.</p>
/// <p>You can submit a prompt by including it in the <code>messages</code> field, specifying the <code>modelId</code> of a foundation model or inference profile to run inference on it, and including any other fields that are relevant to your use case.</p>
/// <p>You can also submit a prompt from Prompt management by specifying the ARN of the prompt version and including a map of variables to values in the <code>promptVariables</code> field. You can append more messages to the prompt by using the <code>messages</code> field. If you use a prompt from Prompt management, you can't include the following fields in the request: <code>additionalModelRequestFields</code>, <code>inferenceConfig</code>, <code>system</code>, or <code>toolConfig</code>. Instead, these fields must be defined through Prompt management. For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-management-use.html">Use a prompt from Prompt management</a>.</p>
/// <p>For information about the Converse API, see <i>Use the Converse API</i> in the <i>Amazon Bedrock User Guide</i>. To use a guardrail, see <i>Use a guardrail with the Converse API</i> in the <i>Amazon Bedrock User Guide</i>. To use a tool with a model, see <i>Tool use (Function calling)</i> in the <i>Amazon Bedrock User Guide</i></p>
/// <p>For example code, see <i>Converse API examples</i> in the <i>Amazon Bedrock User Guide</i>.</p>
/// <p>This operation requires permission for the <code>bedrock:InvokeModel</code> action.</p><important>
/// <p>To deny all inference access to resources that you specify in the modelId field, you need to deny access to the <code>bedrock:InvokeModel</code> and <code>bedrock:InvokeModelWithResponseStream</code> actions. Doing this also denies access to the resource through the base inference actions (<a href="https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModel.html">InvokeModel</a> and <a href="https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModelWithResponseStream.html">InvokeModelWithResponseStream</a>). For more information see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/security_iam_id-based-policy-examples.html#security_iam_id-based-policy-examples-deny-inference">Deny access for inference on specific models</a>.</p>
/// </important>
/// <p>For troubleshooting some of the common errors you might encounter when using the <code>Converse</code> API, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/troubleshooting-api-error-codes.html">Troubleshooting Amazon Bedrock API Error Codes</a> in the Amazon Bedrock User Guide</p>
#[derive(::std::clone::Clone, ::std::fmt::Debug)]
pub struct ConverseFluentBuilder {
handle: ::std::sync::Arc<crate::client::Handle>,
inner: crate::operation::converse::builders::ConverseInputBuilder,
config_override: ::std::option::Option<crate::config::Builder>,
}
impl crate::client::customize::internal::CustomizableSend<crate::operation::converse::ConverseOutput, crate::operation::converse::ConverseError>
for ConverseFluentBuilder
{
fn send(
self,
config_override: crate::config::Builder,
) -> crate::client::customize::internal::BoxFuture<
crate::client::customize::internal::SendResult<crate::operation::converse::ConverseOutput, crate::operation::converse::ConverseError>,
> {
::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
}
}
impl ConverseFluentBuilder {
/// Creates a new `ConverseFluentBuilder`.
pub(crate) fn new(handle: ::std::sync::Arc<crate::client::Handle>) -> Self {
Self {
handle,
inner: ::std::default::Default::default(),
config_override: ::std::option::Option::None,
}
}
/// Access the Converse as a reference.
pub fn as_input(&self) -> &crate::operation::converse::builders::ConverseInputBuilder {
&self.inner
}
/// Sends the request and returns the response.
///
/// If an error occurs, an `SdkError` will be returned with additional details that
/// can be matched against.
///
/// By default, any retryable failures will be retried twice. Retry behavior
/// is configurable with the [RetryConfig](aws_smithy_types::retry::RetryConfig), which can be
/// set when configuring the client.
pub async fn send(
self,
) -> ::std::result::Result<
crate::operation::converse::ConverseOutput,
::aws_smithy_runtime_api::client::result::SdkError<
crate::operation::converse::ConverseError,
::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
>,
> {
let input = self
.inner
.build()
.map_err(::aws_smithy_runtime_api::client::result::SdkError::construction_failure)?;
let runtime_plugins = crate::operation::converse::Converse::operation_runtime_plugins(
self.handle.runtime_plugins.clone(),
&self.handle.conf,
self.config_override,
);
crate::operation::converse::Converse::orchestrate(&runtime_plugins, input).await
}
/// Consumes this builder, creating a customizable operation that can be modified before being sent.
pub fn customize(
self,
) -> crate::client::customize::CustomizableOperation<crate::operation::converse::ConverseOutput, crate::operation::converse::ConverseError, Self>
{
crate::client::customize::CustomizableOperation::new(self)
}
pub(crate) fn config_override(mut self, config_override: impl ::std::convert::Into<crate::config::Builder>) -> Self {
self.set_config_override(::std::option::Option::Some(config_override.into()));
self
}
pub(crate) fn set_config_override(&mut self, config_override: ::std::option::Option<crate::config::Builder>) -> &mut Self {
self.config_override = config_override;
self
}
/// <p>Specifies the model or throughput with which to run inference, or the prompt resource to use in inference. The value depends on the resource that you use:</p>
/// <ul>
/// <li>
/// <p>If you use a base model, specify the model ID or its ARN. For a list of model IDs for base models, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html#model-ids-arns">Amazon Bedrock base model IDs (on-demand throughput)</a> in the Amazon Bedrock User Guide.</p></li>
/// <li>
/// <p>If you use an inference profile, specify the inference profile ID or its ARN. For a list of inference profile IDs, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference-support.html">Supported Regions and models for cross-region inference</a> in the Amazon Bedrock User Guide.</p></li>
/// <li>
/// <p>If you use a provisioned model, specify the ARN of the Provisioned Throughput. For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/prov-thru-use.html">Run inference using a Provisioned Throughput</a> in the Amazon Bedrock User Guide.</p></li>
/// <li>
/// <p>If you use a custom model, first purchase Provisioned Throughput for it. Then specify the ARN of the resulting provisioned model. For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-use.html">Use a custom model in Amazon Bedrock</a> in the Amazon Bedrock User Guide.</p></li>
/// <li>
/// <p>To include a prompt that was defined in <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-management.html">Prompt management</a>, specify the ARN of the prompt version to use.</p></li>
/// </ul>
/// <p>The Converse API doesn't support <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-import-model.html">imported models</a>.</p>
pub fn model_id(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.model_id(input.into());
self
}
/// <p>Specifies the model or throughput with which to run inference, or the prompt resource to use in inference. The value depends on the resource that you use:</p>
/// <ul>
/// <li>
/// <p>If you use a base model, specify the model ID or its ARN. For a list of model IDs for base models, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html#model-ids-arns">Amazon Bedrock base model IDs (on-demand throughput)</a> in the Amazon Bedrock User Guide.</p></li>
/// <li>
/// <p>If you use an inference profile, specify the inference profile ID or its ARN. For a list of inference profile IDs, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference-support.html">Supported Regions and models for cross-region inference</a> in the Amazon Bedrock User Guide.</p></li>
/// <li>
/// <p>If you use a provisioned model, specify the ARN of the Provisioned Throughput. For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/prov-thru-use.html">Run inference using a Provisioned Throughput</a> in the Amazon Bedrock User Guide.</p></li>
/// <li>
/// <p>If you use a custom model, first purchase Provisioned Throughput for it. Then specify the ARN of the resulting provisioned model. For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-use.html">Use a custom model in Amazon Bedrock</a> in the Amazon Bedrock User Guide.</p></li>
/// <li>
/// <p>To include a prompt that was defined in <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-management.html">Prompt management</a>, specify the ARN of the prompt version to use.</p></li>
/// </ul>
/// <p>The Converse API doesn't support <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-import-model.html">imported models</a>.</p>
pub fn set_model_id(mut self, input: ::std::option::Option<::std::string::String>) -> Self {
self.inner = self.inner.set_model_id(input);
self
}
/// <p>Specifies the model or throughput with which to run inference, or the prompt resource to use in inference. The value depends on the resource that you use:</p>
/// <ul>
/// <li>
/// <p>If you use a base model, specify the model ID or its ARN. For a list of model IDs for base models, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html#model-ids-arns">Amazon Bedrock base model IDs (on-demand throughput)</a> in the Amazon Bedrock User Guide.</p></li>
/// <li>
/// <p>If you use an inference profile, specify the inference profile ID or its ARN. For a list of inference profile IDs, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference-support.html">Supported Regions and models for cross-region inference</a> in the Amazon Bedrock User Guide.</p></li>
/// <li>
/// <p>If you use a provisioned model, specify the ARN of the Provisioned Throughput. For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/prov-thru-use.html">Run inference using a Provisioned Throughput</a> in the Amazon Bedrock User Guide.</p></li>
/// <li>
/// <p>If you use a custom model, first purchase Provisioned Throughput for it. Then specify the ARN of the resulting provisioned model. For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-use.html">Use a custom model in Amazon Bedrock</a> in the Amazon Bedrock User Guide.</p></li>
/// <li>
/// <p>To include a prompt that was defined in <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-management.html">Prompt management</a>, specify the ARN of the prompt version to use.</p></li>
/// </ul>
/// <p>The Converse API doesn't support <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-import-model.html">imported models</a>.</p>
pub fn get_model_id(&self) -> &::std::option::Option<::std::string::String> {
self.inner.get_model_id()
}
///
/// Appends an item to `messages`.
///
/// To override the contents of this collection use [`set_messages`](Self::set_messages).
///
/// <p>The messages that you want to send to the model.</p>
pub fn messages(mut self, input: crate::types::Message) -> Self {
self.inner = self.inner.messages(input);
self
}
/// <p>The messages that you want to send to the model.</p>
pub fn set_messages(mut self, input: ::std::option::Option<::std::vec::Vec<crate::types::Message>>) -> Self {
self.inner = self.inner.set_messages(input);
self
}
/// <p>The messages that you want to send to the model.</p>
pub fn get_messages(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::Message>> {
self.inner.get_messages()
}
///
/// Appends an item to `system`.
///
/// To override the contents of this collection use [`set_system`](Self::set_system).
///
/// <p>A prompt that provides instructions or context to the model about the task it should perform, or the persona it should adopt during the conversation.</p>
pub fn system(mut self, input: crate::types::SystemContentBlock) -> Self {
self.inner = self.inner.system(input);
self
}
/// <p>A prompt that provides instructions or context to the model about the task it should perform, or the persona it should adopt during the conversation.</p>
pub fn set_system(mut self, input: ::std::option::Option<::std::vec::Vec<crate::types::SystemContentBlock>>) -> Self {
self.inner = self.inner.set_system(input);
self
}
/// <p>A prompt that provides instructions or context to the model about the task it should perform, or the persona it should adopt during the conversation.</p>
pub fn get_system(&self) -> &::std::option::Option<::std::vec::Vec<crate::types::SystemContentBlock>> {
self.inner.get_system()
}
/// <p>Inference parameters to pass to the model. <code>Converse</code> and <code>ConverseStream</code> support a base set of inference parameters. If you need to pass additional parameters that the model supports, use the <code>additionalModelRequestFields</code> request field.</p>
pub fn inference_config(mut self, input: crate::types::InferenceConfiguration) -> Self {
self.inner = self.inner.inference_config(input);
self
}
/// <p>Inference parameters to pass to the model. <code>Converse</code> and <code>ConverseStream</code> support a base set of inference parameters. If you need to pass additional parameters that the model supports, use the <code>additionalModelRequestFields</code> request field.</p>
pub fn set_inference_config(mut self, input: ::std::option::Option<crate::types::InferenceConfiguration>) -> Self {
self.inner = self.inner.set_inference_config(input);
self
}
/// <p>Inference parameters to pass to the model. <code>Converse</code> and <code>ConverseStream</code> support a base set of inference parameters. If you need to pass additional parameters that the model supports, use the <code>additionalModelRequestFields</code> request field.</p>
pub fn get_inference_config(&self) -> &::std::option::Option<crate::types::InferenceConfiguration> {
self.inner.get_inference_config()
}
/// <p>Configuration information for the tools that the model can use when generating a response.</p>
/// <p>For information about models that support tool use, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html#conversation-inference-supported-models-features">Supported models and model features</a>.</p>
pub fn tool_config(mut self, input: crate::types::ToolConfiguration) -> Self {
self.inner = self.inner.tool_config(input);
self
}
/// <p>Configuration information for the tools that the model can use when generating a response.</p>
/// <p>For information about models that support tool use, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html#conversation-inference-supported-models-features">Supported models and model features</a>.</p>
pub fn set_tool_config(mut self, input: ::std::option::Option<crate::types::ToolConfiguration>) -> Self {
self.inner = self.inner.set_tool_config(input);
self
}
/// <p>Configuration information for the tools that the model can use when generating a response.</p>
/// <p>For information about models that support tool use, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/conversation-inference.html#conversation-inference-supported-models-features">Supported models and model features</a>.</p>
pub fn get_tool_config(&self) -> &::std::option::Option<crate::types::ToolConfiguration> {
self.inner.get_tool_config()
}
/// <p>Configuration information for a guardrail that you want to use in the request. If you include <code>guardContent</code> blocks in the <code>content</code> field in the <code>messages</code> field, the guardrail operates only on those messages. If you include no <code>guardContent</code> blocks, the guardrail operates on all messages in the request body and in any included prompt resource.</p>
pub fn guardrail_config(mut self, input: crate::types::GuardrailConfiguration) -> Self {
self.inner = self.inner.guardrail_config(input);
self
}
/// <p>Configuration information for a guardrail that you want to use in the request. If you include <code>guardContent</code> blocks in the <code>content</code> field in the <code>messages</code> field, the guardrail operates only on those messages. If you include no <code>guardContent</code> blocks, the guardrail operates on all messages in the request body and in any included prompt resource.</p>
pub fn set_guardrail_config(mut self, input: ::std::option::Option<crate::types::GuardrailConfiguration>) -> Self {
self.inner = self.inner.set_guardrail_config(input);
self
}
/// <p>Configuration information for a guardrail that you want to use in the request. If you include <code>guardContent</code> blocks in the <code>content</code> field in the <code>messages</code> field, the guardrail operates only on those messages. If you include no <code>guardContent</code> blocks, the guardrail operates on all messages in the request body and in any included prompt resource.</p>
pub fn get_guardrail_config(&self) -> &::std::option::Option<crate::types::GuardrailConfiguration> {
self.inner.get_guardrail_config()
}
/// <p>Additional inference parameters that the model supports, beyond the base set of inference parameters that <code>Converse</code> and <code>ConverseStream</code> support in the <code>inferenceConfig</code> field. For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html">Model parameters</a>.</p>
pub fn additional_model_request_fields(mut self, input: ::aws_smithy_types::Document) -> Self {
self.inner = self.inner.additional_model_request_fields(input);
self
}
/// <p>Additional inference parameters that the model supports, beyond the base set of inference parameters that <code>Converse</code> and <code>ConverseStream</code> support in the <code>inferenceConfig</code> field. For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html">Model parameters</a>.</p>
pub fn set_additional_model_request_fields(mut self, input: ::std::option::Option<::aws_smithy_types::Document>) -> Self {
self.inner = self.inner.set_additional_model_request_fields(input);
self
}
/// <p>Additional inference parameters that the model supports, beyond the base set of inference parameters that <code>Converse</code> and <code>ConverseStream</code> support in the <code>inferenceConfig</code> field. For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html">Model parameters</a>.</p>
pub fn get_additional_model_request_fields(&self) -> &::std::option::Option<::aws_smithy_types::Document> {
self.inner.get_additional_model_request_fields()
}
///
/// Adds a key-value pair to `promptVariables`.
///
/// To override the contents of this collection use [`set_prompt_variables`](Self::set_prompt_variables).
///
/// <p>Contains a map of variables in a prompt from Prompt management to objects containing the values to fill in for them when running model invocation. This field is ignored if you don't specify a prompt resource in the <code>modelId</code> field.</p>
pub fn prompt_variables(mut self, k: impl ::std::convert::Into<::std::string::String>, v: crate::types::PromptVariableValues) -> Self {
self.inner = self.inner.prompt_variables(k.into(), v);
self
}
/// <p>Contains a map of variables in a prompt from Prompt management to objects containing the values to fill in for them when running model invocation. This field is ignored if you don't specify a prompt resource in the <code>modelId</code> field.</p>
pub fn set_prompt_variables(
mut self,
input: ::std::option::Option<::std::collections::HashMap<::std::string::String, crate::types::PromptVariableValues>>,
) -> Self {
self.inner = self.inner.set_prompt_variables(input);
self
}
/// <p>Contains a map of variables in a prompt from Prompt management to objects containing the values to fill in for them when running model invocation. This field is ignored if you don't specify a prompt resource in the <code>modelId</code> field.</p>
pub fn get_prompt_variables(
&self,
) -> &::std::option::Option<::std::collections::HashMap<::std::string::String, crate::types::PromptVariableValues>> {
self.inner.get_prompt_variables()
}
///
/// Appends an item to `additionalModelResponseFieldPaths`.
///
/// To override the contents of this collection use [`set_additional_model_response_field_paths`](Self::set_additional_model_response_field_paths).
///
/// <p>Additional model parameters field paths to return in the response. <code>Converse</code> and <code>ConverseStream</code> return the requested fields as a JSON Pointer object in the <code>additionalModelResponseFields</code> field. The following is example JSON for <code>additionalModelResponseFieldPaths</code>.</p>
/// <p><code>\[ "/stop_sequence" \]</code></p>
/// <p>For information about the JSON Pointer syntax, see the <a href="https://datatracker.ietf.org/doc/html/rfc6901">Internet Engineering Task Force (IETF)</a> documentation.</p>
/// <p><code>Converse</code> and <code>ConverseStream</code> reject an empty JSON Pointer or incorrectly structured JSON Pointer with a <code>400</code> error code. if the JSON Pointer is valid, but the requested field is not in the model response, it is ignored by <code>Converse</code>.</p>
pub fn additional_model_response_field_paths(mut self, input: impl ::std::convert::Into<::std::string::String>) -> Self {
self.inner = self.inner.additional_model_response_field_paths(input.into());
self
}
/// <p>Additional model parameters field paths to return in the response. <code>Converse</code> and <code>ConverseStream</code> return the requested fields as a JSON Pointer object in the <code>additionalModelResponseFields</code> field. The following is example JSON for <code>additionalModelResponseFieldPaths</code>.</p>
/// <p><code>\[ "/stop_sequence" \]</code></p>
/// <p>For information about the JSON Pointer syntax, see the <a href="https://datatracker.ietf.org/doc/html/rfc6901">Internet Engineering Task Force (IETF)</a> documentation.</p>
/// <p><code>Converse</code> and <code>ConverseStream</code> reject an empty JSON Pointer or incorrectly structured JSON Pointer with a <code>400</code> error code. if the JSON Pointer is valid, but the requested field is not in the model response, it is ignored by <code>Converse</code>.</p>
pub fn set_additional_model_response_field_paths(mut self, input: ::std::option::Option<::std::vec::Vec<::std::string::String>>) -> Self {
self.inner = self.inner.set_additional_model_response_field_paths(input);
self
}
/// <p>Additional model parameters field paths to return in the response. <code>Converse</code> and <code>ConverseStream</code> return the requested fields as a JSON Pointer object in the <code>additionalModelResponseFields</code> field. The following is example JSON for <code>additionalModelResponseFieldPaths</code>.</p>
/// <p><code>\[ "/stop_sequence" \]</code></p>
/// <p>For information about the JSON Pointer syntax, see the <a href="https://datatracker.ietf.org/doc/html/rfc6901">Internet Engineering Task Force (IETF)</a> documentation.</p>
/// <p><code>Converse</code> and <code>ConverseStream</code> reject an empty JSON Pointer or incorrectly structured JSON Pointer with a <code>400</code> error code. if the JSON Pointer is valid, but the requested field is not in the model response, it is ignored by <code>Converse</code>.</p>
pub fn get_additional_model_response_field_paths(&self) -> &::std::option::Option<::std::vec::Vec<::std::string::String>> {
self.inner.get_additional_model_response_field_paths()
}
///
/// Adds a key-value pair to `requestMetadata`.
///
/// To override the contents of this collection use [`set_request_metadata`](Self::set_request_metadata).
///
/// <p>Key-value pairs that you can use to filter invocation logs.</p>
pub fn request_metadata(
mut self,
k: impl ::std::convert::Into<::std::string::String>,
v: impl ::std::convert::Into<::std::string::String>,
) -> Self {
self.inner = self.inner.request_metadata(k.into(), v.into());
self
}
/// <p>Key-value pairs that you can use to filter invocation logs.</p>
pub fn set_request_metadata(
mut self,
input: ::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>>,
) -> Self {
self.inner = self.inner.set_request_metadata(input);
self
}
/// <p>Key-value pairs that you can use to filter invocation logs.</p>
pub fn get_request_metadata(&self) -> &::std::option::Option<::std::collections::HashMap<::std::string::String, ::std::string::String>> {
self.inner.get_request_metadata()
}
/// <p>Model performance settings for the request.</p>
pub fn performance_config(mut self, input: crate::types::PerformanceConfiguration) -> Self {
self.inner = self.inner.performance_config(input);
self
}
/// <p>Model performance settings for the request.</p>
pub fn set_performance_config(mut self, input: ::std::option::Option<crate::types::PerformanceConfiguration>) -> Self {
self.inner = self.inner.set_performance_config(input);
self
}
/// <p>Model performance settings for the request.</p>
pub fn get_performance_config(&self) -> &::std::option::Option<crate::types::PerformanceConfiguration> {
self.inner.get_performance_config()
}
/// <p>Specifies the processing tier configuration used for serving the request.</p>
pub fn service_tier(mut self, input: crate::types::ServiceTier) -> Self {
self.inner = self.inner.service_tier(input);
self
}
/// <p>Specifies the processing tier configuration used for serving the request.</p>
pub fn set_service_tier(mut self, input: ::std::option::Option<crate::types::ServiceTier>) -> Self {
self.inner = self.inner.set_service_tier(input);
self
}
/// <p>Specifies the processing tier configuration used for serving the request.</p>
pub fn get_service_tier(&self) -> &::std::option::Option<crate::types::ServiceTier> {
self.inner.get_service_tier()
}
/// <p>Output configuration for a model response.</p>
pub fn output_config(mut self, input: crate::types::OutputConfig) -> Self {
self.inner = self.inner.output_config(input);
self
}
/// <p>Output configuration for a model response.</p>
pub fn set_output_config(mut self, input: ::std::option::Option<crate::types::OutputConfig>) -> Self {
self.inner = self.inner.set_output_config(input);
self
}
/// <p>Output configuration for a model response.</p>
pub fn get_output_config(&self) -> &::std::option::Option<crate::types::OutputConfig> {
self.inner.get_output_config()
}
}