aws_sdk_bedrockruntime/operation/converse_stream/

builders.rs

// Code generated by software.amazon.smithy.rust.codegen.smithy-rs. DO NOT EDIT.
pub use crate::operation::converse_stream::_converse_stream_output::ConverseStreamOutputBuilder;

pub use crate::operation::converse_stream::_converse_stream_input::ConverseStreamInputBuilder;

impl crate::operation::converse_stream::builders::ConverseStreamInputBuilder {
    /// 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_stream::ConverseStreamOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::converse_stream::ConverseStreamError,
            ::aws_smithy_runtime_api::client::orchestrator::HttpResponse,
        >,
    > {
        let mut fluent_builder = client.converse_stream();
        fluent_builder.inner = self;
        fluent_builder.send().await
    }
}
/// Fluent builder constructing a request to `ConverseStream`.
///
/// <p>Sends messages to the specified Amazon Bedrock model and returns the response in a stream. <code>ConverseStream</code> provides a consistent API that works with all Amazon Bedrock models that support messages. This allows you to write code once and use it with different models. Should a model have unique inference parameters, you can also pass those unique parameters to the model.</p>
/// <p>To find out if a model supports streaming, call <a href="https://docs.aws.amazon.com/bedrock/latest/APIReference/API_GetFoundationModel.html">GetFoundationModel</a> and check the <code>responseStreamingSupported</code> field in the response.</p><note>
/// <p>The CLI doesn't support streaming operations in Amazon Bedrock, including <code>ConverseStream</code>.</p>
/// </note>
/// <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>Conversation streaming example</i> in the <i>Amazon Bedrock User Guide</i>.</p>
/// <p>This operation requires permission for the <code>bedrock:InvokeModelWithResponseStream</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>ConverseStream</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 ConverseStreamFluentBuilder {
    handle: ::std::sync::Arc<crate::client::Handle>,
    inner: crate::operation::converse_stream::builders::ConverseStreamInputBuilder,
    config_override: ::std::option::Option<crate::config::Builder>,
}
impl
    crate::client::customize::internal::CustomizableSend<
        crate::operation::converse_stream::ConverseStreamOutput,
        crate::operation::converse_stream::ConverseStreamError,
    > for ConverseStreamFluentBuilder
{
    fn send(
        self,
        config_override: crate::config::Builder,
    ) -> crate::client::customize::internal::BoxFuture<
        crate::client::customize::internal::SendResult<
            crate::operation::converse_stream::ConverseStreamOutput,
            crate::operation::converse_stream::ConverseStreamError,
        >,
    > {
        ::std::boxed::Box::pin(async move { self.config_override(config_override).send().await })
    }
}
impl ConverseStreamFluentBuilder {
    /// Creates a new `ConverseStreamFluentBuilder`.
    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 ConverseStream as a reference.
    pub fn as_input(&self) -> &crate::operation::converse_stream::builders::ConverseStreamInputBuilder {
        &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_stream::ConverseStreamOutput,
        ::aws_smithy_runtime_api::client::result::SdkError<
            crate::operation::converse_stream::ConverseStreamError,
            ::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_stream::ConverseStream::operation_runtime_plugins(
            self.handle.runtime_plugins.clone(),
            &self.handle.conf,
            self.config_override,
        );
        crate::operation::converse_stream::ConverseStream::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_stream::ConverseStreamOutput,
        crate::operation::converse_stream::ConverseStreamError,
        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 streaming 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 streaming 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 streaming 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::GuardrailStreamConfiguration) -> 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::GuardrailStreamConfiguration>) -> 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::GuardrailStreamConfiguration> {
        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()
    }
}