Invoke Model

bedrockruntime_invoke_model

R Documentation

Invokes the specified Amazon Bedrock model to run inference using the prompt and inference parameters provided in the request body

Description

Invokes the specified Amazon Bedrock model to run inference using the prompt and inference parameters provided in the request body. You use model inference to generate text, images, and embeddings.

For example code, see Invoke model code examples in the Amazon Bedrock User Guide.

This operation requires permission for the bedrock:InvokeModel action.

To deny all inference access to resources that you specify in the modelId field, you need to deny access to the bedrock:InvokeModel and bedrock:InvokeModelWithResponseStream actions. Doing this also denies access to the resource through the Converse API actions (converse and converse_stream). For more information see Deny access for inference on specific models.

For troubleshooting some of the common errors you might encounter when using the invoke_model API, see Troubleshooting Amazon Bedrock API Error Codes in the Amazon Bedrock User Guide

Usage

bedrockruntime_invoke_model(body, contentType, accept, modelId, trace,
  guardrailIdentifier, guardrailVersion, performanceConfigLatency)

Arguments

body	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.
contentType	The MIME type of the input data in the request. You must specify application/json.
accept	The desired MIME type of the inference body in the response. The default value is application/json.
modelId	[required] 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, 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. 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	Specifies whether to enable or disable the Bedrock trace. If enabled, you can see the full Bedrock trace.
guardrailIdentifier	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.
guardrailVersion	The version number for the guardrail. The value can also be DRAFT.
performanceConfigLatency	Model performance settings for the request.

Value

A list with the following syntax:

list(
  body = raw,
  contentType = "string",
  performanceConfigLatency = "standard"|"optimized"
)

Request syntax

svc$invoke_model(
  body = raw,
  contentType = "string",
  accept = "string",
  modelId = "string",
  trace = "ENABLED"|"DISABLED",
  guardrailIdentifier = "string",
  guardrailVersion = "string",
  performanceConfigLatency = "standard"|"optimized"
)