bedrock: Amazon Bedrock

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

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

batch_delete_evaluation_job	Creates a batch deletion job
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 implement safeguards for your generative AI applications
create_guardrail_version	Creates a version of the guardrail
create_model_copy_job	Copies a model to another region so that it can be used there
create_model_customization_job	Creates a fine-tuning job to customize a base model
create_model_import_job	Creates a model import job to import model that you have customized in other environments, such as Amazon SageMaker
create_model_invocation_job	Creates a batch inference job to invoke a model on multiple prompts
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_imported_model	Deletes a custom model that you imported earlier
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_imported_model	Gets properties associated with a customized model you imported
get_inference_profile	Gets information about an inference profile
get_model_copy_job	Retrieves information about a model copy job
get_model_customization_job	Retrieves the properties associated with a model-customization job, including the status of the job
get_model_import_job	Retrieves the properties associated with import model job, including the status of the job
get_model_invocation_job	Gets details about a batch inference 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_imported_models	Returns a list of models you've imported
list_inference_profiles	Returns a list of inference profiles that you can use
list_model_copy_jobs	Returns a list of model copy jobs that you have submitted
list_model_customization_jobs	Returns a list of model customization jobs that you have submitted
list_model_import_jobs	Returns a list of import jobs you've submitted
list_model_invocation_jobs	Lists all batch inference jobs in the account
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
stop_model_invocation_job	Stops a batch inference 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$batch_delete_evaluation_job(
  Foo = 123
)

## End(Not run)

paws.machine.learning documentation built on Sept. 12, 2024, 6:23 a.m.