Client
bedrock | R Documentation |
Amazon Bedrock¶
Description¶
Describes the API operations for creating, managing, fine-turning, and evaluating Amazon Bedrock models.
Usage¶
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 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 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
- 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