R/bedrockruntime_operations.R

In paws.machine.learning: 'Amazon Web Services' Machine Learning Services

# This file is generated by make.paws. Please do not edit here.
#' @importFrom paws.common get_config new_operation new_request send_request
#' @include bedrockruntime_service.R
NULL

#' The action to apply a guardrail
#'
#' @description
#' The action to apply a guardrail.
#'
#' See [https://www.paws-r-sdk.com/docs/bedrockruntime_apply_guardrail/](https://www.paws-r-sdk.com/docs/bedrockruntime_apply_guardrail/) for full documentation.
#'
#' @param guardrailIdentifier [required] The guardrail identifier used in the request to apply the guardrail.
#' @param guardrailVersion [required] The guardrail version used in the request to apply the guardrail.
#' @param source [required] The source of data used in the request to apply the guardrail.
#' @param content [required] The content details used in the request to apply the guardrail.
#'
#' @keywords internal
#'
#' @rdname bedrockruntime_apply_guardrail
bedrockruntime_apply_guardrail <- function(guardrailIdentifier, guardrailVersion, source, content) {
  op <- new_operation(
    name = "ApplyGuardrail",
    http_method = "POST",
    http_path = "/guardrail/{guardrailIdentifier}/version/{guardrailVersion}/apply",
    host_prefix = "",
    paginator = list()
  )
  input <- .bedrockruntime$apply_guardrail_input(guardrailIdentifier = guardrailIdentifier, guardrailVersion = guardrailVersion, source = source, content = content)
  output <- .bedrockruntime$apply_guardrail_output()
  config <- get_config()
  svc <- .bedrockruntime$service(config, op)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.bedrockruntime$operations$apply_guardrail <- bedrockruntime_apply_guardrail

#' Sends messages to the specified Amazon Bedrock model
#'
#' @description
#' Sends messages to the specified Amazon Bedrock model. [`converse`][bedrockruntime_converse] 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.
#'
#' See [https://www.paws-r-sdk.com/docs/bedrockruntime_converse/](https://www.paws-r-sdk.com/docs/bedrockruntime_converse/) for full documentation.
#'
#' @param modelId &#91;required&#93; The identifier for the model that you want to call.
#' 
#' 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)](https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html#model-ids-arns)
#'     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](https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference-support.html)
#'     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](https://docs.aws.amazon.com/bedrock/latest/userguide/prov-thru-use.html)
#'     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](https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-use.html)
#'     in the Amazon Bedrock User Guide.
#' 
#' The Converse API doesn't support [imported
#' models](https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-import-model.html).
#' @param messages &#91;required&#93; The messages that you want to send to the model.
#' @param system A system prompt to pass to the model.
#' @param inferenceConfig Inference parameters to pass to the model.
#' [`converse`][bedrockruntime_converse] supports a base set of inference
#' parameters. If you need to pass additional parameters that the model
#' supports, use the `additionalModelRequestFields` request field.
#' @param toolConfig Configuration information for the tools that the model can use when
#' generating a response.
#' 
#' This field is only supported by Anthropic Claude 3, Cohere Command R,
#' Cohere Command R+, and Mistral Large models.
#' @param guardrailConfig Configuration information for a guardrail that you want to use in the
#' request.
#' @param additionalModelRequestFields Additional inference parameters that the model supports, beyond the base
#' set of inference parameters that [`converse`][bedrockruntime_converse]
#' supports in the `inferenceConfig` field. For more information, see
#' [Model
#' parameters](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html).
#' @param additionalModelResponseFieldPaths Additional model parameters field paths to return in the response.
#' [`converse`][bedrockruntime_converse] returns 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)](https://datatracker.ietf.org/doc/html/rfc6901) documentation.
#' 
#' [`converse`][bedrockruntime_converse] rejects 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`][bedrockruntime_converse].
#'
#' @keywords internal
#'
#' @rdname bedrockruntime_converse
bedrockruntime_converse <- function(modelId, messages, system = NULL, inferenceConfig = NULL, toolConfig = NULL, guardrailConfig = NULL, additionalModelRequestFields = NULL, additionalModelResponseFieldPaths = NULL) {
  op <- new_operation(
    name = "Converse",
    http_method = "POST",
    http_path = "/model/{modelId}/converse",
    host_prefix = "",
    paginator = list()
  )
  input <- .bedrockruntime$converse_input(modelId = modelId, messages = messages, system = system, inferenceConfig = inferenceConfig, toolConfig = toolConfig, guardrailConfig = guardrailConfig, additionalModelRequestFields = additionalModelRequestFields, additionalModelResponseFieldPaths = additionalModelResponseFieldPaths)
  output <- .bedrockruntime$converse_output()
  config <- get_config()
  svc <- .bedrockruntime$service(config, op)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.bedrockruntime$operations$converse <- bedrockruntime_converse

#' Sends messages to the specified Amazon Bedrock model and returns the
#' response in a stream
#'
#' @description
#' Sends messages to the specified Amazon Bedrock model and returns the response in a stream. [`converse_stream`][bedrockruntime_converse_stream] 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.
#'
#' See [https://www.paws-r-sdk.com/docs/bedrockruntime_converse_stream/](https://www.paws-r-sdk.com/docs/bedrockruntime_converse_stream/) for full documentation.
#'
#' @param modelId &#91;required&#93; The ID for the model.
#' 
#' 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)](https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html#model-ids-arns)
#'     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](https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference-support.html)
#'     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](https://docs.aws.amazon.com/bedrock/latest/userguide/prov-thru-use.html)
#'     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](https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-use.html)
#'     in the Amazon Bedrock User Guide.
#' 
#' The Converse API doesn't support [imported
#' models](https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-import-model.html).
#' @param messages &#91;required&#93; The messages that you want to send to the model.
#' @param system A system prompt to send to the model.
#' @param inferenceConfig Inference parameters to pass to the model.
#' [`converse_stream`][bedrockruntime_converse_stream] supports a base set
#' of inference parameters. If you need to pass additional parameters that
#' the model supports, use the `additionalModelRequestFields` request
#' field.
#' @param toolConfig Configuration information for the tools that the model can use when
#' generating a response.
#' 
#' This field is only supported by Anthropic Claude 3 models.
#' @param guardrailConfig Configuration information for a guardrail that you want to use in the
#' request.
#' @param additionalModelRequestFields Additional inference parameters that the model supports, beyond the base
#' set of inference parameters that
#' [`converse_stream`][bedrockruntime_converse_stream] supports in the
#' `inferenceConfig` field.
#' @param additionalModelResponseFieldPaths Additional model parameters field paths to return in the response.
#' [`converse_stream`][bedrockruntime_converse_stream] returns 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)](https://datatracker.ietf.org/doc/html/rfc6901) documentation.
#' 
#' [`converse_stream`][bedrockruntime_converse_stream] rejects 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_stream`][bedrockruntime_converse_stream].
#'
#' @keywords internal
#'
#' @rdname bedrockruntime_converse_stream
bedrockruntime_converse_stream <- function(modelId, messages, system = NULL, inferenceConfig = NULL, toolConfig = NULL, guardrailConfig = NULL, additionalModelRequestFields = NULL, additionalModelResponseFieldPaths = NULL) {
  op <- new_operation(
    name = "ConverseStream",
    http_method = "POST",
    http_path = "/model/{modelId}/converse-stream",
    host_prefix = "",
    paginator = list()
  )
  input <- .bedrockruntime$converse_stream_input(modelId = modelId, messages = messages, system = system, inferenceConfig = inferenceConfig, toolConfig = toolConfig, guardrailConfig = guardrailConfig, additionalModelRequestFields = additionalModelRequestFields, additionalModelResponseFieldPaths = additionalModelResponseFieldPaths)
  output <- .bedrockruntime$converse_stream_output()
  config <- get_config()
  svc <- .bedrockruntime$service(config, op)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.bedrockruntime$operations$converse_stream <- bedrockruntime_converse_stream

#' 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.
#'
#' See [https://www.paws-r-sdk.com/docs/bedrockruntime_invoke_model/](https://www.paws-r-sdk.com/docs/bedrockruntime_invoke_model/) for full documentation.
#'
#' @param body &#91;required&#93; 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](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html).
#' For more information, see [Run
#' inference](https://docs.aws.amazon.com/bedrock/latest/userguide/inference.html)
#' in the Bedrock User Guide.
#' @param contentType The MIME type of the input data in the request. You must specify
#' `application/json`.
#' @param accept The desired MIME type of the inference body in the response. The default
#' value is `application/json`.
#' @param modelId &#91;required&#93; The unique identifier of the model to invoke to run inference.
#' 
#' The `modelId` to provide depends on the type of model 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)](https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html#model-ids-arns)
#'     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](https://docs.aws.amazon.com/bedrock/latest/userguide/prov-thru-use.html)
#'     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](https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-use.html)
#'     in the Amazon Bedrock User Guide.
#' 
#' -   If you use an [imported
#'     model](https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-import-model.html),
#'     specify the ARN of the imported model. You can get the model ARN
#'     from a successful call to
#'     [CreateModelImportJob](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_CreateModelImportJob.html)
#'     or from the Imported models page in the Amazon Bedrock console.
#' @param trace Specifies whether to enable or disable the Bedrock trace. If enabled,
#' you can see the full Bedrock trace.
#' @param 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.
#' @param guardrailVersion The version number for the guardrail. The value can also be `DRAFT`.
#'
#' @keywords internal
#'
#' @rdname bedrockruntime_invoke_model
bedrockruntime_invoke_model <- function(body, contentType = NULL, accept = NULL, modelId, trace = NULL, guardrailIdentifier = NULL, guardrailVersion = NULL) {
  op <- new_operation(
    name = "InvokeModel",
    http_method = "POST",
    http_path = "/model/{modelId}/invoke",
    host_prefix = "",
    paginator = list()
  )
  input <- .bedrockruntime$invoke_model_input(body = body, contentType = contentType, accept = accept, modelId = modelId, trace = trace, guardrailIdentifier = guardrailIdentifier, guardrailVersion = guardrailVersion)
  output <- .bedrockruntime$invoke_model_output()
  config <- get_config()
  svc <- .bedrockruntime$service(config, op)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.bedrockruntime$operations$invoke_model <- bedrockruntime_invoke_model

#' Invoke the specified Amazon Bedrock model to run inference using the
#' prompt and inference parameters provided in the request body
#'
#' @description
#' Invoke the specified Amazon Bedrock model to run inference using the prompt and inference parameters provided in the request body. The response is returned in a stream.
#'
#' See [https://www.paws-r-sdk.com/docs/bedrockruntime_invoke_model_with_response_stream/](https://www.paws-r-sdk.com/docs/bedrockruntime_invoke_model_with_response_stream/) for full documentation.
#'
#' @param body &#91;required&#93; 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](https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html).
#' For more information, see [Run
#' inference](https://docs.aws.amazon.com/bedrock/latest/userguide/inference.html)
#' in the Bedrock User Guide.
#' @param contentType The MIME type of the input data in the request. You must specify
#' `application/json`.
#' @param accept The desired MIME type of the inference body in the response. The default
#' value is `application/json`.
#' @param modelId &#91;required&#93; The unique identifier of the model to invoke to run inference.
#' 
#' The `modelId` to provide depends on the type of model 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)](https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html#model-ids-arns)
#'     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](https://docs.aws.amazon.com/bedrock/latest/userguide/prov-thru-use.html)
#'     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](https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-use.html)
#'     in the Amazon Bedrock User Guide.
#' 
#' -   If you use an [imported
#'     model](https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-import-model.html),
#'     specify the ARN of the imported model. You can get the model ARN
#'     from a successful call to
#'     [CreateModelImportJob](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_CreateModelImportJob.html)
#'     or from the Imported models page in the Amazon Bedrock console.
#' @param trace Specifies whether to enable or disable the Bedrock trace. If enabled,
#' you can see the full Bedrock trace.
#' @param 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 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.
#' @param guardrailVersion The version number for the guardrail. The value can also be `DRAFT`.
#'
#' @keywords internal
#'
#' @rdname bedrockruntime_invoke_model_with_response_stream
bedrockruntime_invoke_model_with_response_stream <- function(body, contentType = NULL, accept = NULL, modelId, trace = NULL, guardrailIdentifier = NULL, guardrailVersion = NULL) {
  op <- new_operation(
    name = "InvokeModelWithResponseStream",
    http_method = "POST",
    http_path = "/model/{modelId}/invoke-with-response-stream",
    host_prefix = "",
    paginator = list()
  )
  input <- .bedrockruntime$invoke_model_with_response_stream_input(body = body, contentType = contentType, accept = accept, modelId = modelId, trace = trace, guardrailIdentifier = guardrailIdentifier, guardrailVersion = guardrailVersion)
  output <- .bedrockruntime$invoke_model_with_response_stream_output()
  config <- get_config()
  svc <- .bedrockruntime$service(config, op)
  request <- new_request(svc, op, input, output)
  response <- send_request(request)
  return(response)
}
.bedrockruntime$operations$invoke_model_with_response_stream <- bedrockruntime_invoke_model_with_response_stream