Struct BedrockClient 

pub struct BedrockClient { /* private fields */ }

Client for Amazon Bedrock Runtime

Client for invoking operations on Amazon Bedrock Runtime. Each operation on Amazon Bedrock Runtime is a method on this this struct. .send() MUST be invoked on the generated operations to dispatch the request to the service.

Constructing a Client

A Config is required to construct a client. For most use cases, the aws-config crate should be used to automatically resolve this config using aws_config::load_from_env(), since this will resolve an SdkConfig which can be shared across multiple different AWS SDK clients. This config resolution process can be customized by calling aws_config::from_env() instead, which returns a ConfigLoader that uses the builder pattern to customize the default config.

In the simplest case, creating a client looks as follows:

let config = aws_config::load_from_env().await;
let client = aws_sdk_bedrockruntime::Client::new(&config);

Occasionally, SDKs may have additional service-specific values that can be set on the Config that is absent from SdkConfig, or slightly different settings for a specific client may be desired. The Builder struct implements From<&SdkConfig>, so setting these specific settings can be done as follows:

let sdk_config = ::aws_config::load_from_env().await;
let config = aws_sdk_bedrockruntime::config::Builder::from(&sdk_config)
    .some_service_specific_setting("value")
    .build();

See the aws-config docs and Config for more information on customizing configuration.

Note: Client construction is expensive due to connection thread pool initialization, and should be done once at application start-up.

Using the Client

A client has a function for every operation that can be performed by the service. For example, the ApplyGuardrail operation has a Client::apply_guardrail, function which returns a builder for that operation. The fluent builder ultimately has a send() function that returns an async future that returns a result, as illustrated below:

let result = client.apply_guardrail()
    .guardrail_identifier("example")
    .send()
    .await;

The underlying HTTP requests that get made by this can be modified with the customize_operation function on the fluent builder. See the customize module for more information.

Implementations

impl Client

pub fn from_conf(conf: Config) -> Client

Creates a new client from the service Config.

Panics

This method will panic in the following cases:

• Retries or timeouts are enabled without a sleep_impl configured.
• Identity caching is enabled without a sleep_impl and time_source configured.
• No behavior_version is provided.

The panic message for each of these will have instructions on how to resolve them.

pub fn config(&self) -> &Config

Returns the client’s configuration.

impl Client

pub fn new(sdk_config: &SdkConfig) -> Client

Creates a new client from an SDK Config.

Panics

• This method will panic if the sdk_config is missing an async sleep implementation. If you experience this panic, set the sleep_impl on the Config passed into this function to fix it.
• This method will panic if the sdk_config is missing an HTTP connector. If you experience this panic, set the http_connector on the Config passed into this function to fix it.
• This method will panic if no BehaviorVersion is provided. If you experience this panic, set behavior_version on the Config or enable the behavior-version-latest Cargo feature.

impl Client

pub fn apply_guardrail(&self) -> ApplyGuardrailFluentBuilder

Constructs a fluent builder for the ApplyGuardrail operation.

• The fluent builder is configurable:
  • guardrail_identifier(impl Into<String>) / set_guardrail_identifier(Option<String>):
    required: true
    

    The guardrail identifier used in the request to apply the guardrail.

    
    
  • guardrail_version(impl Into<String>) / set_guardrail_version(Option<String>):
    required: true
    

    The guardrail version used in the request to apply the guardrail.

    
    
  • source(GuardrailContentSource) / set_source(Option<GuardrailContentSource>):
    required: true
    

    The source of data used in the request to apply the guardrail.

    
    
  • content(GuardrailContentBlock) / set_content(Option<Vec::<GuardrailContentBlock>>):
    required: true
    

    The content details used in the request to apply the guardrail.

    
    
  • output_scope(GuardrailOutputScope) / set_output_scope(Option<GuardrailOutputScope>):
    required: false
    

    Specifies the scope of the output that you get in the response. Set to FULL to return the entire output, including any detected and non-detected entries in the response for enhanced debugging.

    Note that the full output scope doesn’t apply to word filters or regex in sensitive information filters. It does apply to all other filtering policies, including sensitive information with filters that can detect personally identifiable information (PII).

    
    
• On success, responds with ApplyGuardrailOutput with field(s):
  • usage(Option<GuardrailUsage>):

    The usage details in the response from the guardrail.

  • action(GuardrailAction):

    The action taken in the response from the guardrail.

  • action_reason(Option<String>):

    The reason for the action taken when harmful content is detected.

  • outputs(Vec::<GuardrailOutputContent>):

    The output details in the response from the guardrail.

  • assessments(Vec::<GuardrailAssessment>):

    The assessment details in the response from the guardrail.

  • guardrail_coverage(Option<GuardrailCoverage>):

    The guardrail coverage details in the apply guardrail response.

• On failure, responds with SdkError<ApplyGuardrailError>

impl Client

pub fn converse(&self) -> ConverseFluentBuilder

Constructs a fluent builder for the Converse operation.

• The fluent builder is configurable:
  • model_id(impl Into<String>) / set_model_id(Option<String>):
    required: true
    

    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(Message) / set_messages(Option<Vec::<Message>>):
    required: false
    

    The messages that you want to send to the model.

    
    
  • system(SystemContentBlock) / set_system(Option<Vec::<SystemContentBlock>>):
    required: false
    

    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(InferenceConfiguration) / set_inference_config(Option<InferenceConfiguration>):
    required: false
    

    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(ToolConfiguration) / set_tool_config(Option<ToolConfiguration>):
    required: false
    

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

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

    
    
  • guardrail_config(GuardrailConfiguration) / set_guardrail_config(Option<GuardrailConfiguration>):
    required: false
    

    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(Document) / set_additional_model_request_fields(Option<Document>):
    required: false
    

    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(impl Into<String>, PromptVariableValues) / set_prompt_variables(Option<HashMap::<String, PromptVariableValues>>):
    required: false
    

    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(impl Into<String>) / set_additional_model_response_field_paths(Option<Vec::<String>>):
    required: false
    

    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(impl Into<String>, impl Into<String>) / set_request_metadata(Option<HashMap::<String, String>>):
    required: false
    

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

    
    
  • performance_config(PerformanceConfiguration) / set_performance_config(Option<PerformanceConfiguration>):
    required: false
    

    Model performance settings for the request.

    
    
• On success, responds with ConverseOutput with field(s):
  • output(Option<ConverseOutput>):

    The result from the call to Converse.

  • stop_reason(StopReason):

    The reason why the model stopped generating output.

  • usage(Option<TokenUsage>):

    The total number of tokens used in the call to Converse. The total includes the tokens input to the model and the tokens generated by the model.

  • metrics(Option<ConverseMetrics>):

    Metrics for the call to Converse.

  • additional_model_response_fields(Option<Document>):

    Additional fields in the response that are unique to the model.

  • trace(Option<ConverseTrace>):

    A trace object that contains information about the Guardrail behavior.

  • performance_config(Option<PerformanceConfiguration>):

    Model performance settings for the request.

• On failure, responds with SdkError<ConverseError>

impl Client

pub fn converse_stream(&self) -> ConverseStreamFluentBuilder

Constructs a fluent builder for the ConverseStream operation.

• The fluent builder is configurable:
  • model_id(impl Into<String>) / set_model_id(Option<String>):
    required: true
    

    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(Message) / set_messages(Option<Vec::<Message>>):
    required: false
    

    The messages that you want to send to the model.

    
    
  • system(SystemContentBlock) / set_system(Option<Vec::<SystemContentBlock>>):
    required: false
    

    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(InferenceConfiguration) / set_inference_config(Option<InferenceConfiguration>):
    required: false
    

    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(ToolConfiguration) / set_tool_config(Option<ToolConfiguration>):
    required: false
    

    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(GuardrailStreamConfiguration) / set_guardrail_config(Option<GuardrailStreamConfiguration>):
    required: false
    

    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(Document) / set_additional_model_request_fields(Option<Document>):
    required: false
    

    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(impl Into<String>, PromptVariableValues) / set_prompt_variables(Option<HashMap::<String, PromptVariableValues>>):
    required: false
    

    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(impl Into<String>) / set_additional_model_response_field_paths(Option<Vec::<String>>):
    required: false
    

    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(impl Into<String>, impl Into<String>) / set_request_metadata(Option<HashMap::<String, String>>):
    required: false
    

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

    
    
  • performance_config(PerformanceConfiguration) / set_performance_config(Option<PerformanceConfiguration>):
    required: false
    

    Model performance settings for the request.

    
    
• On success, responds with ConverseStreamOutput with field(s):
  • stream(EventReceiver<ConverseStreamOutput, ConverseStreamOutputError>):

    The output stream that the model generated.

• On failure, responds with SdkError<ConverseStreamError>

impl Client

pub fn count_tokens(&self) -> CountTokensFluentBuilder

Constructs a fluent builder for the CountTokens operation.

• The fluent builder is configurable:
  • model_id(impl Into<String>) / set_model_id(Option<String>):
    required: true
    

    The unique identifier or ARN of the foundation model to use for token counting. Each model processes tokens differently, so the token count is specific to the model you specify.

    
    
  • input(CountTokensInput) / set_input(Option<CountTokensInput>):
    required: true
    

    The input for which to count tokens. The structure of this parameter depends on whether you’re counting tokens for an InvokeModel or Converse request:

    • For InvokeModel requests, provide the request body in the invokeModel field

    • For Converse requests, provide the messages and system content in the converse field

    The input format must be compatible with the model specified in the modelId parameter.

    
    
• On success, responds with CountTokensOutput with field(s):
  • input_tokens(i32):

    The number of tokens in the provided input according to the specified model’s tokenization rules. This count represents the number of input tokens that would be processed if the same input were sent to the model in an inference request. Use this value to estimate costs and ensure your inputs stay within model token limits.

• On failure, responds with SdkError<CountTokensError>

impl Client

pub fn get_async_invoke(&self) -> GetAsyncInvokeFluentBuilder

Constructs a fluent builder for the GetAsyncInvoke operation.

• The fluent builder is configurable:
  • invocation_arn(impl Into<String>) / set_invocation_arn(Option<String>):
    required: true
    

    The invocation’s ARN.

    
    
• On success, responds with GetAsyncInvokeOutput with field(s):
  • invocation_arn(String):

    The invocation’s ARN.

  • model_arn(String):

    The invocation’s model ARN.

  • client_request_token(Option<String>):

    The invocation’s idempotency token.

  • status(AsyncInvokeStatus):

    The invocation’s status.

  • failure_message(Option<String>):

    An error message.

  • submit_time(DateTime):

    When the invocation request was submitted.

  • last_modified_time(Option<DateTime>):

    The invocation’s last modified time.

  • end_time(Option<DateTime>):

    When the invocation ended.

  • output_data_config(Option<AsyncInvokeOutputDataConfig>):

    Output data settings.

• On failure, responds with SdkError<GetAsyncInvokeError>

impl Client

pub fn invoke_model(&self) -> InvokeModelFluentBuilder

Constructs a fluent builder for the InvokeModel operation.

• The fluent builder is configurable:
  • body(Blob) / set_body(Option<Blob>):
    required: false
    

    The prompt and inference parameters in the format specified in the contentType in the header. You must provide the body in JSON format. To see the format and content of the request and response bodies for different models, refer to Inference parameters. For more information, see Run inference in the Bedrock User Guide.

    
    
  • content_type(impl Into<String>) / set_content_type(Option<String>):
    required: false
    

    The MIME type of the input data in the request. You must specify application/json.

    
    
  • accept(impl Into<String>) / set_accept(Option<String>):
    required: false
    

    The desired MIME type of the inference body in the response. The default value is application/json.

    
    
  • model_id(impl Into<String>) / set_model_id(Option<String>):
    required: true
    

    The unique identifier of the model to invoke to run inference.

    The modelId to provide depends on the type of model or throughput 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, specify the ARN of the custom model deployment (for on-demand inference) or the ARN of your provisioned model (for Provisioned Throughput). For more information, see Use a custom model in Amazon Bedrock in the Amazon Bedrock User Guide.

    • If you use an imported model, specify the ARN of the imported model. You can get the model ARN from a successful call to CreateModelImportJob or from the Imported models page in the Amazon Bedrock console.

    
    
  • trace(Trace) / set_trace(Option<Trace>):
    required: false
    

    Specifies whether to enable or disable the Bedrock trace. If enabled, you can see the full Bedrock trace.

    
    
  • guardrail_identifier(impl Into<String>) / set_guardrail_identifier(Option<String>):
    required: false
    

    The unique identifier of the guardrail that you want to use. If you don’t provide a value, no guardrail is applied to the invocation.

    An error will be thrown in the following situations.

    • You don’t provide a guardrail identifier but you specify the amazon-bedrock-guardrailConfig field in the request body.

    • You enable the guardrail but the contentType isn’t application/json.

    • You provide a guardrail identifier, but guardrailVersion isn’t specified.

    
    
  • guardrail_version(impl Into<String>) / set_guardrail_version(Option<String>):
    required: false
    

    The version number for the guardrail. The value can also be DRAFT.

    
    
  • performance_config_latency(PerformanceConfigLatency) / set_performance_config_latency(Option<PerformanceConfigLatency>):
    required: false
    

    Model performance settings for the request.

    
    
• On success, responds with InvokeModelOutput with field(s):
  • body(Blob):

    Inference response from the model in the format specified in the contentType header. To see the format and content of the request and response bodies for different models, refer to Inference parameters.

  • content_type(String):

    The MIME type of the inference result.

  • performance_config_latency(Option<PerformanceConfigLatency>):

    Model performance settings for the request.

• On failure, responds with SdkError<InvokeModelError>

impl Client

pub fn invoke_model_with_bidirectional_stream( &self, ) -> InvokeModelWithBidirectionalStreamFluentBuilder

Constructs a fluent builder for the InvokeModelWithBidirectionalStream operation.

• The fluent builder is configurable:
  • model_id(impl Into<String>) / set_model_id(Option<String>):
    required: true
    

    The model ID or ARN of the model ID to use. Currently, only amazon.nova-sonic-v1:0 is supported.

    
    
  • body(EventStreamSender<InvokeModelWithBidirectionalStreamInput, InvokeModelWithBidirectionalStreamInputError>) / set_body(EventStreamSender<InvokeModelWithBidirectionalStreamInput, InvokeModelWithBidirectionalStreamInputError>):
    required: true
    

    The prompt and inference parameters in the format specified in the BidirectionalInputPayloadPart in the header. You must provide the body in JSON format. To see the format and content of the request and response bodies for different models, refer to Inference parameters. For more information, see Run inference in the Bedrock User Guide.

    
    
• On success, responds with InvokeModelWithBidirectionalStreamOutput with field(s):
  • body(EventReceiver<InvokeModelWithBidirectionalStreamOutput, InvokeModelWithBidirectionalStreamOutputError>):

    Streaming response from the model in the format specified by the BidirectionalOutputPayloadPart header.

• On failure, responds with SdkError<InvokeModelWithBidirectionalStreamError>

impl Client

pub fn invoke_model_with_response_stream( &self, ) -> InvokeModelWithResponseStreamFluentBuilder

Constructs a fluent builder for the InvokeModelWithResponseStream operation.

• The fluent builder is configurable:
  • body(Blob) / set_body(Option<Blob>):
    required: false
    

    The prompt and inference parameters in the format specified in the contentType in the header. You must provide the body in JSON format. To see the format and content of the request and response bodies for different models, refer to Inference parameters. For more information, see Run inference in the Bedrock User Guide.

    
    
  • content_type(impl Into<String>) / set_content_type(Option<String>):
    required: false
    

    The MIME type of the input data in the request. You must specify application/json.

    
    
  • accept(impl Into<String>) / set_accept(Option<String>):
    required: false
    

    The desired MIME type of the inference body in the response. The default value is application/json.

    
    
  • model_id(impl Into<String>) / set_model_id(Option<String>):
    required: true
    

    The unique identifier of the model to invoke to run inference.

    The modelId to provide depends on the type of model or throughput 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, specify the ARN of the custom model deployment (for on-demand inference) or the ARN of your provisioned model (for Provisioned Throughput). For more information, see Use a custom model in Amazon Bedrock in the Amazon Bedrock User Guide.

    • If you use an imported model, specify the ARN of the imported model. You can get the model ARN from a successful call to CreateModelImportJob or from the Imported models page in the Amazon Bedrock console.

    
    
  • trace(Trace) / set_trace(Option<Trace>):
    required: false
    

    Specifies whether to enable or disable the Bedrock trace. If enabled, you can see the full Bedrock trace.

    
    
  • guardrail_identifier(impl Into<String>) / set_guardrail_identifier(Option<String>):
    required: false
    

    The unique identifier of the guardrail that you want to use. If you don’t provide a value, no guardrail is applied to the invocation.

    An error is thrown in the following situations.

    • You don’t provide a guardrail identifier but you specify the amazon-bedrock-guardrailConfig field in the request body.

    • You enable the guardrail but the contentType isn’t application/json.

    • You provide a guardrail identifier, but guardrailVersion isn’t specified.

    
    
  • guardrail_version(impl Into<String>) / set_guardrail_version(Option<String>):
    required: false
    

    The version number for the guardrail. The value can also be DRAFT.

    
    
  • performance_config_latency(PerformanceConfigLatency) / set_performance_config_latency(Option<PerformanceConfigLatency>):
    required: false
    

    Model performance settings for the request.

    
    
• On success, responds with InvokeModelWithResponseStreamOutput with field(s):
  • body(EventReceiver<ResponseStream, ResponseStreamError>):

    Inference response from the model in the format specified by the contentType header. To see the format and content of this field for different models, refer to Inference parameters.

  • content_type(String):

    The MIME type of the inference result.

  • performance_config_latency(Option<PerformanceConfigLatency>):

    Model performance settings for the request.

• On failure, responds with SdkError<InvokeModelWithResponseStreamError>

impl Client

pub fn list_async_invokes(&self) -> ListAsyncInvokesFluentBuilder

Constructs a fluent builder for the ListAsyncInvokes operation. This operation supports pagination; See into_paginator().

• The fluent builder is configurable:
  • submit_time_after(DateTime) / set_submit_time_after(Option<DateTime>):
    required: false
    

    Include invocations submitted after this time.

    
    
  • submit_time_before(DateTime) / set_submit_time_before(Option<DateTime>):
    required: false
    

    Include invocations submitted before this time.

    
    
  • status_equals(AsyncInvokeStatus) / set_status_equals(Option<AsyncInvokeStatus>):
    required: false
    

    Filter invocations by status.

    
    
  • max_results(i32) / set_max_results(Option<i32>):
    required: false
    

    The maximum number of invocations to return in one page of results.

    
    
  • next_token(impl Into<String>) / set_next_token(Option<String>):
    required: false
    

    Specify the pagination token from a previous request to retrieve the next page of results.

    
    
  • sort_by(SortAsyncInvocationBy) / set_sort_by(Option<SortAsyncInvocationBy>):
    required: false
    

    How to sort the response.

    
    
  • sort_order(SortOrder) / set_sort_order(Option<SortOrder>):
    required: false
    

    The sorting order for the response.

    
    
• On success, responds with ListAsyncInvokesOutput with field(s):
  • next_token(Option<String>):

    Specify the pagination token from a previous request to retrieve the next page of results.

  • async_invoke_summaries(Option<Vec::<AsyncInvokeSummary>>):

    A list of invocation summaries.

• On failure, responds with SdkError<ListAsyncInvokesError>

impl Client

pub fn start_async_invoke(&self) -> StartAsyncInvokeFluentBuilder

Constructs a fluent builder for the StartAsyncInvoke operation.

• The fluent builder is configurable:
  • client_request_token(impl Into<String>) / set_client_request_token(Option<String>):
    required: false
    

    Specify idempotency token to ensure that requests are not duplicated.

    
    
  • model_id(impl Into<String>) / set_model_id(Option<String>):
    required: true
    

    The model to invoke.

    
    
  • model_input(Document) / set_model_input(Option<Document>):
    required: true
    

    Input to send to the model.

    
    
  • output_data_config(AsyncInvokeOutputDataConfig) / set_output_data_config(Option<AsyncInvokeOutputDataConfig>):
    required: true
    

    Where to store the output.

    
    
  • tags(Tag) / set_tags(Option<Vec::<Tag>>):
    required: false
    

    Tags to apply to the invocation.

    
    
• On success, responds with StartAsyncInvokeOutput with field(s):
  • invocation_arn(String):

    The ARN of the invocation.

• On failure, responds with SdkError<StartAsyncInvokeError>

Trait Implementations

impl Clone for Client

fn clone(&self) -> Client

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for Client

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

Formats the value using the given formatter.