AWS.Bedrock — AWS SDK for JavaScript (original) (raw)

Property Details

endpoint ⇒ AWS.Endpoint

Returns an Endpoint object representing the endpoint URL for service requests.

Method Details

batchDeleteEvaluationJob(params = {}, callback) ⇒ AWS.Request

Creates a batch deletion job. A model evaluation job can only be deleted if it has following status FAILED, COMPLETED, and STOPPED. You can request up to 25 model evaluation jobs be deleted in a single request.

createEvaluationJob(params = {}, callback) ⇒ AWS.Request

API operation for creating and managing Amazon Bedrock automatic model evaluation jobs and model evaluation jobs that use human workers. To learn more about the requirements for creating a model evaluation job see, Model evaluation.

createGuardrail(params = {}, callback) ⇒ AWS.Request

Creates a guardrail to block topics and to implement safeguards for your generative AI applications.

You can configure the following policies in a guardrail to avoid undesirable and harmful content, filter out denied topics and words, and remove sensitive information for privacy protection.

• Content filters - Adjust filter strengths to block input prompts or model responses containing harmful content.
• Denied topics - Define a set of topics that are undesirable in the context of your application. These topics will be blocked if detected in user queries or model responses.
• Word filters - Configure filters to block undesirable words, phrases, and profanity. Such words can include offensive terms, competitor names etc.
• Sensitive information filters - Block or mask sensitive information such as personally identifiable information (PII) or custom regex in user inputs and model responses.

In addition to the above policies, you can also configure the messages to be returned to the user if a user input or model response is in violation of the policies defined in the guardrail.

For more information, see Guardrails for Amazon Bedrock in the Amazon Bedrock User Guide.

createGuardrailVersion(params = {}, callback) ⇒ AWS.Request

Creates a version of the guardrail. Use this API to create a snapshot of the guardrail when you are satisfied with a configuration, or to compare the configuration with another version.

createModelCopyJob(params = {}, callback) ⇒ AWS.Request

createModelCustomizationJob(params = {}, callback) ⇒ AWS.Request

Creates a fine-tuning job to customize a base model.

You specify the base foundation model and the location of the training data. After the model-customization job completes successfully, your custom model resource will be ready to use. Amazon Bedrock returns validation loss metrics and output generations after the job completes.

For information on the format of training and validation data, see Prepare the datasets.

Model-customization jobs are asynchronous and the completion time depends on the base model and the training/validation data size. To monitor a job, use the GetModelCustomizationJob operation to retrieve the job status.

For more information, see Custom models in the Amazon Bedrock User Guide.

createModelImportJob(params = {}, callback) ⇒ AWS.Request

Creates a model import job to import model that you have customized in other environments, such as Amazon SageMaker. For more information, see Import a customized model

createModelInvocationJob(params = {}, callback) ⇒ AWS.Request

createProvisionedModelThroughput(params = {}, callback) ⇒ AWS.Request

deleteCustomModel(params = {}, callback) ⇒ AWS.Request

deleteGuardrail(params = {}, callback) ⇒ AWS.Request

Deletes a guardrail.

• To delete a guardrail, only specify the ARN of the guardrail in the guardrailIdentifier field. If you delete a guardrail, all of its versions will be deleted.
• To delete a version of a guardrail, specify the ARN of the guardrail in the guardrailIdentifier field and the version in the guardrailVersion field.

deleteImportedModel(params = {}, callback) ⇒ AWS.Request

deleteModelInvocationLoggingConfiguration(params = {}, callback) ⇒ AWS.Request

Delete the invocation logging.

deleteProvisionedModelThroughput(params = {}, callback) ⇒ AWS.Request

getCustomModel(params = {}, callback) ⇒ AWS.Request

getEvaluationJob(params = {}, callback) ⇒ AWS.Request

Retrieves the properties associated with a model evaluation job, including the status of the job. For more information, see Model evaluation.

getFoundationModel(params = {}, callback) ⇒ AWS.Request

Get details about a Amazon Bedrock foundation model.

getGuardrail(params = {}, callback) ⇒ AWS.Request

Gets details about a guardrail. If you don't specify a version, the response returns details for the DRAFT version.

getImportedModel(params = {}, callback) ⇒ AWS.Request

Gets properties associated with a customized model you imported.

getInferenceProfile(params = {}, callback) ⇒ AWS.Request

Gets information about an inference profile. For more information, see the Amazon Bedrock User Guide.

getModelCopyJob(params = {}, callback) ⇒ AWS.Request

getModelCustomizationJob(params = {}, callback) ⇒ AWS.Request

getModelImportJob(params = {}, callback) ⇒ AWS.Request

getModelInvocationJob(params = {}, callback) ⇒ AWS.Request

getModelInvocationLoggingConfiguration(params = {}, callback) ⇒ AWS.Request

Get the current configuration values for model invocation logging.

getProvisionedModelThroughput(params = {}, callback) ⇒ AWS.Request

listCustomModels(params = {}, callback) ⇒ AWS.Request

listEvaluationJobs(params = {}, callback) ⇒ AWS.Request

Lists model evaluation jobs.

listFoundationModels(params = {}, callback) ⇒ AWS.Request

listGuardrails(params = {}, callback) ⇒ AWS.Request

Lists details about all the guardrails in an account. To list the DRAFT version of all your guardrails, don't specify the guardrailIdentifier field. To list all versions of a guardrail, specify the ARN of the guardrail in the guardrailIdentifier field.

You can set the maximum number of results to return in a response in the maxResults field. If there are more results than the number you set, the response returns a nextToken that you can send in another ListGuardrails request to see the next batch of results.

listImportedModels(params = {}, callback) ⇒ AWS.Request

listInferenceProfiles(params = {}, callback) ⇒ AWS.Request

Returns a list of inference profiles that you can use.

listModelCopyJobs(params = {}, callback) ⇒ AWS.Request

listModelCustomizationJobs(params = {}, callback) ⇒ AWS.Request

Returns a list of model customization jobs that you have submitted. You can filter the jobs to return based on one or more criteria.

For more information, see Custom models in the Amazon Bedrock User Guide.

listModelImportJobs(params = {}, callback) ⇒ AWS.Request

listModelInvocationJobs(params = {}, callback) ⇒ AWS.Request

listProvisionedModelThroughputs(params = {}, callback) ⇒ AWS.Request

listTagsForResource(params = {}, callback) ⇒ AWS.Request

putModelInvocationLoggingConfiguration(params = {}, callback) ⇒ AWS.Request

Set the configuration values for model invocation logging.

stopEvaluationJob(params = {}, callback) ⇒ AWS.Request

Stops an in progress model evaluation job.

stopModelCustomizationJob(params = {}, callback) ⇒ AWS.Request

stopModelInvocationJob(params = {}, callback) ⇒ AWS.Request

Stops a batch inference job. You're only charged for tokens that were already processed. For more information, see Stop a batch inference job.

untagResource(params = {}, callback) ⇒ AWS.Request

updateGuardrail(params = {}, callback) ⇒ AWS.Request

Updates a guardrail with the values you specify.

• Specify a name and optional description.
• Specify messages for when the guardrail successfully blocks a prompt or a model response in the blockedInputMessaging and blockedOutputsMessaging fields.
• Specify topics for the guardrail to deny in the topicPolicyConfig object. Each GuardrailTopicConfig object in the topicsConfig list pertains to one topic.
  • Give a name and description so that the guardrail can properly identify the topic.
  • Specify DENY in the type field.
  • (Optional) Provide up to five prompts that you would categorize as belonging to the topic in the examples list.
• Specify filter strengths for the harmful categories defined in Amazon Bedrock in the contentPolicyConfig object. Each GuardrailContentFilterConfig object in the filtersConfig list pertains to a harmful category. For more information, see Content filters. For more information about the fields in a content filter, see GuardrailContentFilterConfig.
  • Specify the category in the type field.
  • Specify the strength of the filter for prompts in the inputStrength field and for model responses in the strength field of the GuardrailContentFilterConfig.
• (Optional) For security, include the ARN of a KMS key in the kmsKeyId field.

updateProvisionedModelThroughput(params = {}, callback) ⇒ AWS.Request

waitFor(state, params = {}, callback) ⇒ AWS.Request

Waits for a given Bedrock resource. The final callback or'complete' event will be fired only when the resource is either in its final state or the waiter has timed out and stopped polling for the final state.