Struct ConverseStreamInput 

#[non_exhaustive]
pub struct ConverseStreamInput {
    pub model_id: Option<String>,
    pub messages: Option<Vec<Message>>,
    pub system: Option<Vec<SystemContentBlock>>,
    pub inference_config: Option<InferenceConfiguration>,
    pub tool_config: Option<ToolConfiguration>,
    pub guardrail_config: Option<GuardrailStreamConfiguration>,
    pub additional_model_request_fields: Option<Document>,
    pub prompt_variables: Option<HashMap<String, PromptVariableValues>>,
    pub additional_model_response_field_paths: Option<Vec<String>>,
    pub request_metadata: Option<HashMap<String, String>>,
    pub performance_config: Option<PerformanceConfiguration>,
}

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

model_id: Option<String>

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:

• If you use a base model, specify the model ID or its ARN. For a list of model IDs for base models, see Amazon Bedrock base model IDs (on-demand throughput) in the Amazon Bedrock User Guide.

• If you use an inference profile, specify the inference profile ID or its ARN. For a list of inference profile IDs, see Supported Regions and models for cross-region inference in the Amazon Bedrock User Guide.

• If you use a provisioned model, specify the ARN of the Provisioned Throughput. For more information, see Run inference using a Provisioned Throughput in the Amazon Bedrock User Guide.

• 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 Use a custom model in Amazon Bedrock in the Amazon Bedrock User Guide.

• To include a prompt that was defined in Prompt management, specify the ARN of the prompt version to use.

The Converse API doesn't support imported models.

messages: Option<Vec<Message>>

The messages that you want to send to the model.

system: Option<Vec<SystemContentBlock>>

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.

inference_config: Option<InferenceConfiguration>

Inference parameters to pass to the model. Converse and ConverseStream support a base set of inference parameters. If you need to pass additional parameters that the model supports, use the additionalModelRequestFields request field.

tool_config: Option<ToolConfiguration>

Configuration information for the tools that the model can use when generating a response.

For information about models that support streaming tool use, see Supported models and model features.

guardrail_config: Option<GuardrailStreamConfiguration>

Configuration information for a guardrail that you want to use in the request. If you include guardContent blocks in the content field in the messages field, the guardrail operates only on those messages. If you include no guardContent blocks, the guardrail operates on all messages in the request body and in any included prompt resource.

additional_model_request_fields: Option<Document>

Additional inference parameters that the model supports, beyond the base set of inference parameters that Converse and ConverseStream support in the inferenceConfig field. For more information, see Model parameters.

prompt_variables: Option<HashMap<String, PromptVariableValues>>

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 modelId field.

additional_model_response_field_paths: Option<Vec<String>>

Additional model parameters field paths to return in the response. Converse and ConverseStream return the requested fields as a JSON Pointer object in the additionalModelResponseFields field. The following is example JSON for additionalModelResponseFieldPaths.

\[ "/stop_sequence" \]

For information about the JSON Pointer syntax, see the Internet Engineering Task Force (IETF) documentation.

Converse and ConverseStream reject an empty JSON Pointer or incorrectly structured JSON Pointer with a 400 error code. if the JSON Pointer is valid, but the requested field is not in the model response, it is ignored by Converse.

request_metadata: Option<HashMap<String, String>>

Key-value pairs that you can use to filter invocation logs.

performance_config: Option<PerformanceConfiguration>

Model performance settings for the request.

Implementations

impl ConverseStreamInput

pub fn model_id(&self) -> Option<&str>

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:

• If you use a base model, specify the model ID or its ARN. For a list of model IDs for base models, see Amazon Bedrock base model IDs (on-demand throughput) in the Amazon Bedrock User Guide.

• If you use an inference profile, specify the inference profile ID or its ARN. For a list of inference profile IDs, see Supported Regions and models for cross-region inference in the Amazon Bedrock User Guide.

• If you use a provisioned model, specify the ARN of the Provisioned Throughput. For more information, see Run inference using a Provisioned Throughput in the Amazon Bedrock User Guide.

• 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 Use a custom model in Amazon Bedrock in the Amazon Bedrock User Guide.

• To include a prompt that was defined in Prompt management, specify the ARN of the prompt version to use.

The Converse API doesn't support imported models.

pub fn messages(&self) -> &[Message]

The messages that you want to send to the model.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .messages.is_none().

pub fn system(&self) -> &[SystemContentBlock]

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.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .system.is_none().

pub fn inference_config(&self) -> Option<&InferenceConfiguration>

Inference parameters to pass to the model. Converse and ConverseStream support a base set of inference parameters. If you need to pass additional parameters that the model supports, use the additionalModelRequestFields request field.

pub fn tool_config(&self) -> Option<&ToolConfiguration>

Configuration information for the tools that the model can use when generating a response.

For information about models that support streaming tool use, see Supported models and model features.

pub fn guardrail_config(&self) -> Option<&GuardrailStreamConfiguration>

Configuration information for a guardrail that you want to use in the request. If you include guardContent blocks in the content field in the messages field, the guardrail operates only on those messages. If you include no guardContent blocks, the guardrail operates on all messages in the request body and in any included prompt resource.

pub fn additional_model_request_fields(&self) -> Option<&Document>

Additional inference parameters that the model supports, beyond the base set of inference parameters that Converse and ConverseStream support in the inferenceConfig field. For more information, see Model parameters.

pub fn prompt_variables(&self) -> Option<&HashMap<String, PromptVariableValues>>

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 modelId field.

pub fn additional_model_response_field_paths(&self) -> &[String]

Additional model parameters field paths to return in the response. Converse and ConverseStream return the requested fields as a JSON Pointer object in the additionalModelResponseFields field. The following is example JSON for additionalModelResponseFieldPaths.

\[ "/stop_sequence" \]

For information about the JSON Pointer syntax, see the Internet Engineering Task Force (IETF) documentation.

Converse and ConverseStream reject an empty JSON Pointer or incorrectly structured JSON Pointer with a 400 error code. if the JSON Pointer is valid, but the requested field is not in the model response, it is ignored by Converse.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .additional_model_response_field_paths.is_none().

pub fn request_metadata(&self) -> Option<&HashMap<String, String>>

Key-value pairs that you can use to filter invocation logs.

pub fn performance_config(&self) -> Option<&PerformanceConfiguration>

Model performance settings for the request.

impl ConverseStreamInput

pub fn builder() -> ConverseStreamInputBuilder

Creates a new builder-style object to manufacture ConverseStreamInput.

Trait Implementations

impl Clone for ConverseStreamInput

fn clone(&self) -> ConverseStreamInput

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for ConverseStreamInput

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl PartialEq for ConverseStreamInput

fn eq(&self, other: &ConverseStreamInput) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for ConverseStreamInput