bedrock {paws.machine.learning}

Amazon Bedrock

Description

Describes the API operations for creating, managing, fine-turning, and evaluating Amazon Bedrock models.

Usage

bedrock(config = list(), credentials = list(), endpoint = NULL, region = NULL)

Arguments

config

Optional configuration of credentials, endpoint, and/or region.

• credentials:

  • creds:

    • access_key_id: AWS access key ID

    • secret_access_key: AWS secret access key

    • session_token: AWS temporary session token

  • profile: The name of a profile to use. If not given, then the default profile is used.

  • anonymous: Set anonymous credentials.

• endpoint: The complete URL to use for the constructed client.

• region: The AWS Region used in instantiating the client.

• close_connection: Immediately close all HTTP connections.

• timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.

• s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.

• sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

• creds:

  • access_key_id: AWS access key ID

  • secret_access_key: AWS secret access key

  • session_token: AWS temporary session token

• profile: The name of a profile to use. If not given, then the default profile is used.

• anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax

svc <- bedrock(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations

create_evaluation_job	API operation for creating and managing Amazon Bedrock automatic model evaluation jobs and model evaluation jobs that use human workers
create_guardrail	Creates a guardrail to block topics and to filter out harmful content
create_guardrail_version	Creates a version of the guardrail
create_model_customization_job	Creates a fine-tuning job to customize a base model
create_provisioned_model_throughput	Creates dedicated throughput for a base or custom model with the model units and for the duration that you specify
delete_custom_model	Deletes a custom model that you created earlier
delete_guardrail	Deletes a guardrail
delete_model_invocation_logging_configuration	Delete the invocation logging
delete_provisioned_model_throughput	Deletes a Provisioned Throughput
get_custom_model	Get the properties associated with a Amazon Bedrock custom model that you have created
get_evaluation_job	Retrieves the properties associated with a model evaluation job, including the status of the job
get_foundation_model	Get details about a Amazon Bedrock foundation model
get_guardrail	Gets details about a guardrail
get_model_customization_job	Retrieves the properties associated with a model-customization job, including the status of the job
get_model_invocation_logging_configuration	Get the current configuration values for model invocation logging
get_provisioned_model_throughput	Returns details for a Provisioned Throughput
list_custom_models	Returns a list of the custom models that you have created with the CreateModelCustomizationJob operation
list_evaluation_jobs	Lists model evaluation jobs
list_foundation_models	Lists Amazon Bedrock foundation models that you can use
list_guardrails	Lists details about all the guardrails in an account
list_model_customization_jobs	Returns a list of model customization jobs that you have submitted
list_provisioned_model_throughputs	Lists the Provisioned Throughputs in the account
list_tags_for_resource	List the tags associated with the specified resource
put_model_invocation_logging_configuration	Set the configuration values for model invocation logging
stop_evaluation_job	Stops an in progress model evaluation job
stop_model_customization_job	Stops an active model customization job
tag_resource	Associate tags with a resource
untag_resource	Remove one or more tags from a resource
update_guardrail	Updates a guardrail with the values you specify
update_provisioned_model_throughput	Updates the name or associated model for a Provisioned Throughput

Examples

## Not run: 
svc <- bedrock()
svc$create_evaluation_job(
  Foo = 123
)

## End(Not run)