bedrock: Amazon Bedrock

View source: R/bedrock_service.R

bedrockR Documentation

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)


paws.machine.learning documentation built on May 29, 2024, 9:43 a.m.