Client

bedrock

R 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

batch_delete_advanced_prompt_optimization_job	Batch delete the specified advanced prompt optimization jobs
batch_delete_evaluation_job	Deletes a batch of evaluation jobs
cancel_automated_reasoning_policy_build_workflow	Cancels a running Automated Reasoning policy build workflow
create_advanced_prompt_optimization_job	Creates an asynchronous batch job for advanced prompt optimization
create_automated_reasoning_policy	Creates an Automated Reasoning policy for Amazon Bedrock Guardrails
create_automated_reasoning_policy_test_case	Creates a test for an Automated Reasoning policy
create_automated_reasoning_policy_version	Creates a new version of an existing Automated Reasoning policy
create_custom_model	Creates a new custom model in Amazon Bedrock
create_custom_model_deployment	Deploys a custom model for on-demand inference in Amazon Bedrock
create_evaluation_job	Creates an evaluation job
create_foundation_model_agreement	Request a model access agreement for the specified model
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_inference_profile	Creates an application inference profile to track metrics and costs when invoking a model
create_marketplace_model_endpoint	Creates an endpoint for a model from Amazon Bedrock Marketplace
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_prompt_router	Creates a prompt router that manages the routing of requests between multiple foundation models based on the routing criteria
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_automated_reasoning_policy	Deletes an Automated Reasoning policy or policy version
delete_automated_reasoning_policy_build_workflow	Deletes an Automated Reasoning policy build workflow and its associated artifacts
delete_automated_reasoning_policy_test_case	Deletes an Automated Reasoning policy test
delete_custom_model	Deletes a custom model that you created earlier
delete_custom_model_deployment	Deletes a custom model deployment
delete_enforced_guardrail_configuration	Deletes the account-level enforced guardrail configuration
delete_foundation_model_agreement	Delete the model access agreement for the specified model
delete_guardrail	Deletes a guardrail
delete_imported_model	Deletes a custom model that you imported earlier
delete_inference_profile	Deletes an application inference profile
delete_marketplace_model_endpoint	Deletes an endpoint for a model from Amazon Bedrock Marketplace
delete_model_invocation_logging_configuration	Delete the invocation logging
delete_prompt_router	Deletes a specified prompt router
delete_provisioned_model_throughput	Deletes a Provisioned Throughput
delete_resource_policy	Deletes a previously created Bedrock resource policy
deregister_marketplace_model_endpoint	Deregisters an endpoint for a model from Amazon Bedrock Marketplace
export_automated_reasoning_policy_version	Exports the policy definition for an Automated Reasoning policy version
get_advanced_prompt_optimization_job	Retrieves the details and status of an advanced prompt optimization job
get_automated_reasoning_policy	Retrieves details about an Automated Reasoning policy or policy version
get_automated_reasoning_policy_annotations	Retrieves the current annotations for an Automated Reasoning policy build workflow
get_automated_reasoning_policy_build_workflow	Retrieves detailed information about an Automated Reasoning policy build workflow, including its status, configuration, and metadata
get_automated_reasoning_policy_build_workflow_result_assets	Retrieves the resulting assets from a completed Automated Reasoning policy build workflow, including build logs, quality reports, and generated policy artifacts
get_automated_reasoning_policy_next_scenario	Retrieves the next test scenario for validating an Automated Reasoning policy
get_automated_reasoning_policy_test_case	Retrieves details about a specific Automated Reasoning policy test
get_automated_reasoning_policy_test_result	Retrieves the test result for a specific Automated Reasoning policy test
get_custom_model	Get the properties associated with a Amazon Bedrock custom model that you have created
get_custom_model_deployment	Retrieves information about a custom model deployment, including its status, configuration, and metadata
get_evaluation_job	Gets information about an evaluation job, such as the status of the job
get_foundation_model	Get details about a Amazon Bedrock foundation model
get_foundation_model_availability	Get information about the Foundation model availability
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_marketplace_model_endpoint	Retrieves details about a specific endpoint for a model from Amazon Bedrock Marketplace
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_prompt_router	Retrieves details about a prompt router
get_provisioned_model_throughput	Returns details for a Provisioned Throughput
get_resource_policy	Gets the resource policy document for a Bedrock resource
get_use_case_for_model_access	Get usecase for model access
list_advanced_prompt_optimization_jobs	Lists all advanced prompt optimization jobs for the account
list_automated_reasoning_policies	Lists all Automated Reasoning policies in your account, with optional filtering by policy ARN
list_automated_reasoning_policy_build_workflows	Lists all build workflows for an Automated Reasoning policy, showing the history of policy creation and modification attempts
list_automated_reasoning_policy_test_cases	Lists tests for an Automated Reasoning policy
list_automated_reasoning_policy_test_results	Lists test results for an Automated Reasoning policy, showing how the policy performed against various test scenarios and validation checks
list_custom_model_deployments	Lists custom model deployments in your account
list_custom_models	Returns a list of the custom models that you have created with the CreateModelCustomizationJob operation
list_enforced_guardrails_configuration	Lists the account-level enforced guardrail configurations
list_evaluation_jobs	Lists all existing evaluation jobs
list_foundation_model_agreement_offers	Get the offers associated with the specified model
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_marketplace_model_endpoints	Lists the endpoints for models from Amazon Bedrock Marketplace in your Amazon Web Services account
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_prompt_routers	Retrieves a list of prompt routers
list_provisioned_model_throughputs	Lists the Provisioned Throughputs in the account
list_tags_for_resource	List the tags associated with the specified resource
put_enforced_guardrail_configuration	Sets the account-level enforced guardrail configuration
put_model_invocation_logging_configuration	Set the configuration values for model invocation logging
put_resource_policy	Adds a resource policy for a Bedrock resource
put_use_case_for_model_access	Put usecase for model access
register_marketplace_model_endpoint	Registers an existing Amazon SageMaker endpoint with Amazon Bedrock Marketplace, allowing it to be used with Amazon Bedrock APIs
start_automated_reasoning_policy_build_workflow	Starts a new build workflow for an Automated Reasoning policy
start_automated_reasoning_policy_test_workflow	Initiates a test workflow to validate Automated Reasoning policy tests
stop_advanced_prompt_optimization_job	Stops an in-progress advanced prompt optimization job
stop_evaluation_job	Stops an evaluation job that is current being created or running
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_automated_reasoning_policy	Updates an existing Automated Reasoning policy with new rules, variables, or configuration
update_automated_reasoning_policy_annotations	Updates the annotations for an Automated Reasoning policy build workflow
update_automated_reasoning_policy_test_case	Updates an existing Automated Reasoning policy test
update_custom_model_deployment	Updates a custom model deployment with a new custom model
update_guardrail	Updates a guardrail with the values you specify
update_marketplace_model_endpoint	Updates the configuration of an existing endpoint for a model from Amazon Bedrock Marketplace
update_provisioned_model_throughput	Updates the name or associated model for a Provisioned Throughput

Examples

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

## End(Not run)