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Selective content guarding controls for enforced guardrails.
Model-specific information for the enforced guardrail configuration.
Account-level enforced guardrail input configuration.
Account enforced guardrail output configuration.
Input data configuration for the advanced prompt optimization job.
ARN of the advanced prompt optimization job.
Description of the advanced prompt optimization job.
ARN or ID of the advanced prompt optimization job.
Name of the advanced prompt optimization job.
Summary of an advanced prompt optimization job.
Output data configuration for the advanced prompt optimization job.
Information about the agreement availability
The evaluator model used in knowledge base evaluation job or in model evaluation job that use a model as judge. This model computes all evaluation related metrics.
Specifies the model configuration for the evaluator model. EvaluatorModelConfig is required for evaluation jobs that use a knowledge base or in model evaluation job that use a model as judge. This model computes all evaluation related metrics.
The location in Amazon S3 where your prompt dataset is stored.
Used to specify the name of a built-in prompt dataset and optionally, the Amazon S3 bucket where a custom prompt dataset is saved.
Defines the prompt datasets, built-in metric names and custom metric names, and the task type.
Defines the model you want to evaluate custom metrics in an Amazon Bedrock evaluation job.
Configuration of the evaluator model you want to use to evaluate custom metrics in an Amazon Bedrock evaluation job.
Defines the value for one rating in a custom metric rating scale.
Defines the value and corresponding definition for one rating in a custom metric rating scale.
The definition of a custom metric for use in an Amazon Bedrock evaluation job. A custom metric definition includes a metric name, prompt (instructions) and optionally, a rating scale. Your prompt must include a task description and input variables. The required input variables are different for model-as-a-judge and RAG evaluations. For more information about how to define a custom metric in Amazon Bedrock, see Create a prompt for a custom metrics (LLM-as-a-judge model evaluations) and Create a prompt for a custom metrics (RAG evaluations).
An array item definining a single custom metric for use in an Amazon Bedrock evaluation job.
Defines the configuration of custom metrics to be used in an evaluation job. To learn more about using custom metrics in Amazon Bedrock evaluation jobs, see Create a prompt for a custom metrics (LLM-as-a-judge model evaluations) and Create a prompt for a custom metrics (RAG evaluations).
The configuration details of an automated evaluation job. The EvaluationDatasetMetricConfig object is used to specify the prompt datasets, task type, and metric names.
Represents a logical statement that can be expressed both in formal logic notation and natural language, providing dual representations for better understanding and validation.
Represents a logical scenario where claims can be evaluated as true or false, containing specific logical assignments.
References a portion of the original input text that corresponds to logical elements.
Contains the logical translation of natural language input into formal logical statements, including premises, claims, and confidence scores.
References a specific automated reasoning policy rule that was applied during evaluation.
Identifies logical issues in the translated statements that exist independent of any policy rules, such as statements that are always true or always false.
Indicates that the claims are definitively true and logically implied by the premises, with no possible alternative interpretations.
Represents one possible logical interpretation of ambiguous input content.
Indicates that the input has multiple valid logical interpretations, requiring additional context or clarification.
Indicates that the input exceeds the processing capacity due to the volume or complexity of the logical information.
Indicates that the claims could be either true or false depending on additional assumptions not provided in the input.
Indicates that no relevant logical information could be extracted from the input for validation.
Indicates that the claims are logically false and contradictory to the established rules or premises.
Indicates that no valid claims can be made due to logical contradictions in the premises or rules.
Represents the result of an Automated Reasoning validation check, indicating whether the content is logically valid, invalid, or falls into other categories based on the policy rules.
An annotation for adding a new rule to an Automated Reasoning policy using a formal logical expression.
An annotation for adding a new rule to the policy by converting a natural language description into a formal logical expression.
Represents a formal logic rule in an Automated Reasoning policy. For example, rules can be expressed as if-then statements that define logical constraints.
A mutation operation that adds a new rule to the policy definition during the build process.
Represents a single value within a custom type definition, including its identifier and description.
An annotation for adding a new custom type to an Automated Reasoning policy, defining a set of possible values for variables.
Represents a custom user-defined viarble type in an Automated Reasoning policy. Types are enum-based and provide additional context beyond predefined variable types.
A mutation operation that adds a new custom type to the policy definition during the build process.
Represents a single value that can be added to an existing custom type in the policy.
An annotation for adding a new variable to an Automated Reasoning policy, which can be used in rule expressions.
Represents a variable in an Automated Reasoning policy. Variables represent concepts that can have values assigned during natural language translation.
A mutation operation that adds a new variable to the policy definition during the build process.
Represents a single line of text from a source document, annotated with its line number for precise referencing.
Represents a content element within an annotated chunk. This union type allows for different types of content elements to be included in document chunks, such as individual lines of text with their line numbers.
Represents a portion of a source document with line number annotations. Chunks help organize document content for easier navigation and reference.
An annotation for modifying an existing variable in an Automated Reasoning policy.
Represents a modification to a value within an existing custom type.
Represents a value to be removed from an existing custom type in the policy.
An annotation for managing values within custom types, including adding, updating, or removing specific type values.
An annotation for modifying an existing custom type in an Automated Reasoning policy.
An annotation for modifying an existing rule in an Automated Reasoning policy.
An annotation for updating the policy based on feedback about how it performed on specific test scenarios.
An annotation for updating the policy based on feedback about how specific rules performed during testing or real-world usage.
An annotation for processing and incorporating new content into an Automated Reasoning policy.
An annotation for removing a variable from an Automated Reasoning policy.
An annotation for removing a custom type from an Automated Reasoning policy.
An annotation for removing a rule from an Automated Reasoning policy.
Contains the various operations that can be performed on an Automated Reasoning policy, including adding, updating, and deleting rules, variables, and types.
Describes the location of a statement within a source document using line numbers.
Represents a single, indivisible statement extracted from a source document. Atomic statements are the fundamental units used to ground policy rules and variables to their source material.
Represents a single element in an Automated Reasoning policy definition, such as a rule, variable, or type definition.
Represents a message generated during a build step, providing information about what happened or any issues encountered.
Represents the planning phase of policy build workflow, where the system analyzes source content and determines what operations to perform.
A mutation operation that modifies an existing variable in the policy definition during the build process.
A mutation operation that modifies an existing custom type in the policy definition during the build process.
A mutation operation that modifies an existing rule in the policy definition during the build process.
A mutation operation that removes a variable from the policy definition during the build process.
A mutation operation that removes a custom type from the policy definition during the build process.
A mutation operation that removes a rule from the policy definition during the build process.
A container for various mutation operations that can be applied to an Automated Reasoning policy, including adding, updating, and deleting policy elements.
Provides context about what type of operation was being performed during a build step.
Represents a single step in the policy build process, containing context about what was being processed and any messages or results.
Represents a single entry in the policy build log, containing information about a specific step or event in the build process.
Contains detailed logging information about the policy build process, including steps taken, decisions made, and any issues encountered.
Represents a single entry in the asset manifest, describing one artifact produced by the build workflow.
A catalog of all artifacts produced by a build workflow, providing a comprehensive list of available assets including their types and identifiers.
Represents a source document that was processed during a build workflow. Contains the document content, metadata, and a hash for verification.
Represents a test scenario used to validate an Automated Reasoning policy, including the test conditions and expected outcomes.
Contains a comprehensive entity encompassing all the scenarios generated by the build workflow, which can be used to validate an Automated Reasoning policy.
Represents a generated test case, consisting of query content, guard content, and expected results.
Contains a comprehensive test suite generated by the build workflow, providing validation capabilities for automated reasoning policies.
References a specific atomic statement within a source document, used to link policy elements back to their source material.
Provides detailed fidelity analysis for a specific policy variable, including which source document statements support it and how accurate the variable definition is.
Provides detailed fidelity analysis for a specific policy rule, including which source document statements support it and how accurate the rule is.
Represents a source document that was analyzed during fidelity report generation, including the document's metadata and its content broken down into atomic statements.
A comprehensive analysis report that measures how accurately a generated policy represents the source documents. The report includes coverage and accuracy scores, detailed grounding information linking policy elements to source statements, and annotated document content.
Represents a set of rules that operate on completely separate variables, indicating they address different concerns or domains within the policy.
Associates a type name with a specific value name, used for referencing type values in rules and other policy elements.
Provides a comprehensive analysis of the quality and completeness of an Automated Reasoning policy definition, highlighting potential issues and optimization opportunities.
Contains the formal logic rules, variables, and custom variable types that define an Automated Reasoning policy. The policy definition specifies the constraints used to validate foundation model responses for accuracy and logical consistency.
Contains the various assets generated during a policy build workflow, including logs, quality reports, test cases, and the final policy definition.
Represents a source document used in the policy build workflow, containing the content and metadata needed for policy generation.
Contains content and instructions for repairing or improving an existing Automated Reasoning policy.
Configuration for generating a fidelity report, which can either analyze new documents or update an existing fidelity report with a new policy definition.
Defines the content and configuration for different types of policy build workflows.
Defines the source content for a policy build workflow, which can include documents, repair instructions, or other input materials.
Provides a summary of a policy build workflow, including its current status, timing information, and key identifiers.
Contains summary information about an Automated Reasoning policy, including metadata and timestamps.
Represents a test for validating an Automated Reasoning policy. tests contain sample inputs and expected outcomes to verify policy behavior.
Contains the results of testing an Automated Reasoning policy against various scenarios and validation checks.
Batch deletion error for an advanced prompt optimization job.
Successfully deleted advanced prompt optimization job.
Batch Delete Advanced Prompt Optimization Jobs Request
Input validation failed. Check your request parameters and retry the request.
The number of requests exceeds the limit. Resubmit your request later.
An internal server error occurred. Retry your request.
Batch Delete Advanced Prompt Optimization Jobs Response
A JSON array that provides the status of the evaluation jobs being deleted.
An evaluation job for deletion, and it’s current status.
Deletes a batch of evaluation jobs. An 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.
The specified resource Amazon Resource Name (ARN) was not found. Check the Amazon Resource Name (ARN) and try your request again.
Error occurred because of a conflict while performing an operation.
Deletes a batch of evaluation jobs. An 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.
Contains the document contained in the wrapper object, along with its attributes/fields.
Cancels a running Automated Reasoning policy build workflow. This stops the policy generation process and prevents further processing of the source documents.
Cancels a running Automated Reasoning policy build workflow. This stops the policy generation process and prevents further processing of the source documents.
CloudWatch logging configuration.
Inference configuration for a model.
Configuration for a model used in advanced prompt optimization.
Create Advanced Prompt Optimization Job Request
The request contains more tags than can be associated with a resource (50 tags per resource). The maximum number of tags includes both existing tags and those included in your current request.
The number of requests exceeds the service quota. Resubmit your request later.
Create Advanced Prompt Optimization Job Response
Creates an Automated Reasoning policy for Amazon Bedrock Guardrails. Automated Reasoning policies use mathematical techniques to detect hallucinations, suggest corrections, and highlight unstated assumptions in the responses of your GenAI application. To create a policy, you upload a source document that describes the rules that you're encoding. Automated Reasoning extracts important concepts from the source document that will become variables in the policy and infers policy rules.
Creates an Automated Reasoning policy for Amazon Bedrock Guardrails. Automated Reasoning policies use mathematical techniques to detect hallucinations, suggest corrections, and highlight unstated assumptions in the responses of your GenAI application. To create a policy, you upload a source document that describes the rules that you're encoding. Automated Reasoning extracts important concepts from the source document that will become variables in the policy and infers policy rules.
Creates a test for an Automated Reasoning policy. Tests validate that your policy works as expected by providing sample inputs and expected outcomes. Use tests to verify policy behavior before deploying to production.
Creates a test for an Automated Reasoning policy. Tests validate that your policy works as expected by providing sample inputs and expected outcomes. Use tests to verify policy behavior before deploying to production.
Creates a new version of an existing Automated Reasoning policy. This allows you to iterate on your policy rules while maintaining previous versions for rollback or comparison purposes.
Creates a new version of an existing Automated Reasoning policy. This allows you to iterate on your policy rules while maintaining previous versions for rollback or comparison purposes.
Deploys a custom model for on-demand inference in Amazon Bedrock. After you deploy your custom model, you use the deployment's Amazon Resource Name (ARN) as the modelId parameter when you submit prompts and generate responses with model inference. For more information about setting up on-demand inference for custom models, see Set up inference for a custom model. The following actions are related to the CreateCustomModelDeployment operation: GetCustomModelDeployment ListCustomModelDeployments DeleteCustomModelDeployment
Deploys a custom model for on-demand inference in Amazon Bedrock. After you deploy your custom model, you use the deployment's Amazon Resource Name (ARN) as the modelId parameter when you submit prompts and generate responses with model inference. For more information about setting up on-demand inference for custom models, see Set up inference for a custom model. The following actions are related to the CreateCustomModelDeployment operation: GetCustomModelDeployment ListCustomModelDeployments DeleteCustomModelDeployment
The Amazon S3 data source of the model to import.
The data source of the model to import.
Creates a new custom model in Amazon Bedrock. After the model is active, you can use it for inference. To use the model for inference, you must purchase Provisioned Throughput for it. You can't use On-demand inference with these custom models. For more information about Provisioned Throughput, see Provisioned Throughput. The model appears in ListCustomModels with a customizationType of imported. To track the status of the new model, you use the GetCustomModel API operation. The model can be in the following states: Creating - Initial state during validation and registration Active - Model is ready for use in inference Failed - Creation process encountered an error Related APIs GetCustomModel ListCustomModels DeleteCustomModel
Creates a new custom model in Amazon Bedrock. After the model is active, you can use it for inference. To use the model for inference, you must purchase Provisioned Throughput for it. You can't use On-demand inference with these custom models. For more information about Provisioned Throughput, see Provisioned Throughput. The model appears in ListCustomModels with a customizationType of imported. To track the status of the new model, you use the GetCustomModel API operation. The model can be in the following states: Creating - Initial state during validation and registration Active - Model is ready for use in inference Failed - Creation process encountered an error Related APIs GetCustomModel ListCustomModels DeleteCustomModel
The Amazon S3 location where the results of your evaluation job are saved.
Configuration for the Amazon Bedrock foundation model used for reranking vector search results. This specifies which model to use and any additional parameters required by the model.
Specifies a field to be used during the reranking process in a Knowledge Base vector search. This structure identifies metadata fields that should be considered when reordering search results to improve relevance.
Configuration for selectively including or excluding metadata fields during the reranking process. This allows you to control which metadata attributes are considered when reordering search results.
Configuration for how metadata should be used during the reranking process in Knowledge Base vector searches. This determines which metadata fields are included or excluded when reordering search results.
Configuration for using Amazon Bedrock foundation models to rerank Knowledge Base vector search results. This enables more sophisticated relevance ranking using large language models.
Configuration for reranking vector search results to improve relevance. Reranking applies additional relevance models to reorder the initial vector search results based on more sophisticated criteria.
Specifies the name of the metadata attribute/field to apply filters. You must match the name of the attribute/field in your data source/document metadata.
module RetrievalFilter : sig ... endSpecifies the filters to use on the metadata attributes/fields in the knowledge base data sources before returning results.
module RetrievalFilterList : sig ... endDefines the schema for a metadata attribute used in Knowledge Base vector searches. Metadata attributes provide additional context for documents and can be used for filtering and reranking search results.
Configuration for implicit filtering in Knowledge Base vector searches. Implicit filtering allows you to automatically filter search results based on metadata attributes without requiring explicit filter expressions in each query.
The configuration details for returning the results from the knowledge base vector search.
Contains configuration details for retrieving information from a knowledge base.
The configuration details for retrieving information from a knowledge base.
The configuration details for transforming the prompt.
The configuration details for the model to process the prompt prior to retrieval and response generation.
The template for the prompt that's sent to the model for response generation.
The configuration details for text generation using a language model via the RetrieveAndGenerate function.
Contains configuration details of the inference for knowledge base retrieval and response generation.
The configuration details for the guardrail.
The configuration details for response generation based on retrieved text chunks.
Contains configuration details for retrieving information from a knowledge base and generating responses.
The response generation configuration of the external source wrapper object.
The unique wrapper object of the document from the S3 location.
The unique external source of the content contained in the wrapper object.
The configuration of the external source wrapper object in the retrieveAndGenerate function.
Contains configuration details for a knowledge base retrieval and response generation.
The configuration details for retrieving information from a knowledge base and generating responses.
A summary of a RAG source used for a retrieve-only Knowledge Base evaluation job where you provide your own inference response data.
A summary of a RAG source used for a retrieve-and-generate Knowledge Base evaluation job where you provide your own inference response data.
A summary of a RAG source used for a Knowledge Base evaluation job where you provide your own inference response data.
Contains configuration details for retrieval of information and response generation.
A summary of a model used for a model evaluation job where you provide your own inference response data.
Contains performance settings for a model.
Contains the ARN of the Amazon Bedrock model or inference profile specified in your evaluation job. Each Amazon Bedrock model supports different inferenceParams. To learn more about supported inference parameters for Amazon Bedrock models, see Inference parameters for foundation models. The inferenceParams are specified using JSON. To successfully insert JSON as string make sure that all quotations are properly escaped. For example, "temperature":"0.25" key value pair would need to be formatted as \"temperature\":\"0.25\" to successfully accepted in the request.
Defines the models used in the model evaluation job.
The configuration details of the inference model for an evaluation job. For automated model evaluation jobs, only a single model is supported. For human-based model evaluation jobs, your annotator can compare the responses for up to two different models.
Contains SageMakerFlowDefinition object. The object is used to specify the prompt dataset, task type, rating method and metric names.
In a model evaluation job that uses human workers you must define the name of the metric, and how you want that metric rated ratingMethod, and an optional description of the metric.
Specifies the custom metrics, how tasks will be rated, the flow definition ARN, and your custom prompt datasets. Model evaluation jobs use human workers only support the use of custom prompt datasets. To learn more about custom prompt datasets and the required format, see Custom prompt datasets. When you create custom metrics in HumanEvaluationCustomMetric you must specify the metric's name. The list of names specified in the HumanEvaluationCustomMetric array, must match the metricNames array of strings specified in EvaluationDatasetMetricConfig. For example, if in the HumanEvaluationCustomMetric array your specified the names "accuracy", "toxicity", "readability" as custom metrics then the metricNames array would need to look like the following ["accuracy", "toxicity", "readability"] in EvaluationDatasetMetricConfig.
The configuration details of either an automated or human-based evaluation job.
Creates an evaluation job.
Creates an evaluation job.
Request a model access agreement for the specified model.
Request a model access agreement for the specified model.
A word to configure for the guardrail.
The managed word list to configure for the guardrail.
Contains details about the word policy to configured for the guardrail.
The tier that your guardrail uses for denied topic filters. Consider using a tier that balances performance, accuracy, and compatibility with your existing generative AI workflows.
Details about topics for the guardrail to identify and deny.
Contains details about topics that the guardrail should identify and deny.
The regular expression to configure for the guardrail.
The PII entity to configure for the guardrail.
Contains details about PII entities and regular expressions to configure for the guardrail.
The system-defined guardrail profile that you're using with your guardrail. Guardrail profiles define the destination Amazon Web Services Regions where guardrail inference requests can be automatically routed. Using guardrail profiles helps maintain guardrail performance and reliability when demand increases. For more information, see the Amazon Bedrock User Guide.
The filter configuration details for the guardrails contextual grounding filter.
The policy configuration details for the guardrails contextual grounding policy.
The tier that your guardrail uses for content filters. Consider using a tier that balances performance, accuracy, and compatibility with your existing generative AI workflows.
Contains filter strengths for harmful content. Guardrails support the following content filters to detect and filter harmful user inputs and FM-generated outputs. Hate – Describes language or a statement that discriminates, criticizes, insults, denounces, or dehumanizes a person or group on the basis of an identity (such as race, ethnicity, gender, religion, sexual orientation, ability, and national origin). Insults – Describes language or a statement that includes demeaning, humiliating, mocking, insulting, or belittling language. This type of language is also labeled as bullying. Sexual – Describes language or a statement that indicates sexual interest, activity, or arousal using direct or indirect references to body parts, physical traits, or sex. Violence – Describes language or a statement that includes glorification of or threats to inflict physical pain, hurt, or injury toward a person, group or thing. Content filtering depends on the confidence classification of user inputs and FM responses across each of the four harmful categories. All input and output statements are classified into one of four confidence levels (NONE, LOW, MEDIUM, HIGH) for each harmful category. For example, if a statement is classified as Hate with HIGH confidence, the likelihood of the statement representing hateful content is high. A single statement can be classified across multiple categories with varying confidence levels. For example, a single statement can be classified as Hate with HIGH confidence, Insults with LOW confidence, Sexual with NONE confidence, and Violence with MEDIUM confidence. For more information, see Guardrails content filters.
Contains details about how to handle harmful content.
Configuration settings for integrating Automated Reasoning policies with Amazon Bedrock Guardrails.
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 Amazon Bedrock Guardrails in the Amazon Bedrock User Guide.
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 Amazon Bedrock Guardrails in the Amazon Bedrock User Guide.
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.
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.
Contains information about the model or system-defined inference profile that is the source for an inference profile..
Creates an application inference profile to track metrics and costs when invoking a model. To create an application inference profile for a foundation model in one region, specify the ARN of the model in that region. To create an application inference profile for a foundation model across multiple regions, specify the ARN of the system-defined inference profile that contains the regions that you want to route requests to. For more information, see Increase throughput and resilience with cross-region inference in Amazon Bedrock. in the Amazon Bedrock User Guide.
Creates an application inference profile to track metrics and costs when invoking a model. To create an application inference profile for a foundation model in one region, specify the ARN of the model in that region. To create an application inference profile for a foundation model across multiple regions, specify the ARN of the system-defined inference profile that contains the regions that you want to route requests to. For more information, see Increase throughput and resilience with cross-region inference in Amazon Bedrock. in the Amazon Bedrock User Guide.
The configuration of a virtual private cloud (VPC). For more information, see Protect your data using Amazon Virtual Private Cloud and Amazon Web Services PrivateLink.
Specifies the configuration for a Amazon SageMaker endpoint.
Specifies the configuration for the endpoint.
Creates an endpoint for a model from Amazon Bedrock Marketplace. The endpoint is hosted by Amazon SageMaker.
Contains details about an endpoint for a model from Amazon Bedrock Marketplace.
Creates an endpoint for a model from Amazon Bedrock Marketplace. The endpoint is hosted by Amazon SageMaker.
Copies a model to another region so that it can be used there. For more information, see Copy models to be used in other regions in the Amazon Bedrock User Guide.
Copies a model to another region so that it can be used there. For more information, see Copy models to be used in other regions in the Amazon Bedrock User Guide.
Array of up to 10 validators.
A mapping of a metadata key to a value that it should or should not equal.
Rules for filtering invocation logs. A filter can be a mapping of a metadata key to a value that it should or should not equal (a base filter), or a list of base filters that are all applied with AND or OR logical operators
A storage location for invocation logs.
Settings for using invocation logs to customize a model.
S3 Location of the training data.
S3 Location of the output data.
Hyperparameters for controlling the reinforcement fine-tuning training process, including learning settings and evaluation intervals.
Configuration for using an AWS Lambda function to grade model responses during reinforcement fine-tuning training.
Configuration for the grader used in reinforcement fine-tuning to evaluate model responses and provide reward signals.
Configuration settings for reinforcement fine-tuning (RFT), including grader configuration and training hyperparameters.
Details about a teacher model used for model customization.
Settings for distilling a foundation model into a smaller and more efficient model.
A model customization configuration
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.
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.
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
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
Contains the configuration of the S3 location of the output data.
Contains the configuration of the S3 location of the output data.
Contains the configuration of the S3 location of the input data.
Details about the location of the input to the batch inference job.
Creates a batch inference job to invoke a model on multiple prompts. Format your data according to Format your inference data and upload it to an Amazon S3 bucket. For more information, see Process multiple prompts with batch inference. The response returns a jobArn that you can use to stop or get details about the job.
Creates a batch inference job to invoke a model on multiple prompts. Format your data according to Format your inference data and upload it to an Amazon S3 bucket. For more information, see Process multiple prompts with batch inference. The response returns a jobArn that you can use to stop or get details about the job.
Routing criteria for a prompt router.
The target model for a prompt router.
Creates a prompt router that manages the routing of requests between multiple foundation models based on the routing criteria.
Creates a prompt router that manages the routing of requests between multiple foundation models based on the routing criteria.
Creates dedicated throughput for a base or custom model with the model units and for the duration that you specify. For pricing details, see Amazon Bedrock Pricing. For more information, see Provisioned Throughput in the Amazon Bedrock User Guide.
Creates dedicated throughput for a base or custom model with the model units and for the duration that you specify. For pricing details, see Amazon Bedrock Pricing. For more information, see Provisioned Throughput in the Amazon Bedrock User Guide.
Contains summary information about a custom model deployment, including its ARN, name, status, and associated custom model.
Details about an update to a custom model deployment, including the new custom model resource ARN and current update status.
Summary information for a custom model.
A CustomModelUnit (CMU) is an abstract view of the hardware utilization that Amazon Bedrock needs to host a single copy of your custom model. A model copy represents a single instance of your imported model that is ready to serve inference requests. Amazon Bedrock determines the number of custom model units that a model copy needs when you import the custom model. You can use CustomModelUnits to estimate the cost of running your custom model. For more information, see Calculate the cost of running a custom model in the Amazon Bedrock user guide.
For a Distillation job, the status details for the data processing sub-task of the job.
Deletes an Automated Reasoning policy build workflow and its associated artifacts. This permanently removes the workflow history and any generated assets.
Thrown when attempting to delete or modify a resource that is currently being used by other resources or operations. For example, trying to delete an Automated Reasoning policy that is referenced by an active guardrail.
Deletes an Automated Reasoning policy build workflow and its associated artifacts. This permanently removes the workflow history and any generated assets.
Deletes an Automated Reasoning policy or policy version. This operation is idempotent. If you delete a policy more than once, each call succeeds. Deleting a policy removes it permanently and cannot be undone.
Deletes an Automated Reasoning policy or policy version. This operation is idempotent. If you delete a policy more than once, each call succeeds. Deleting a policy removes it permanently and cannot be undone.
Deletes an Automated Reasoning policy test. This operation is idempotent; if you delete a test more than once, each call succeeds.
Deletes an Automated Reasoning policy test. This operation is idempotent; if you delete a test more than once, each call succeeds.
Deletes a custom model deployment. This operation stops the deployment and removes it from your account. After deletion, the deployment ARN can no longer be used for inference requests. The following actions are related to the DeleteCustomModelDeployment operation: CreateCustomModelDeployment GetCustomModelDeployment ListCustomModelDeployments
Deletes a custom model deployment. This operation stops the deployment and removes it from your account. After deletion, the deployment ARN can no longer be used for inference requests. The following actions are related to the DeleteCustomModelDeployment operation: CreateCustomModelDeployment GetCustomModelDeployment ListCustomModelDeployments
Deletes a custom model that you created earlier. For more information, see Custom models in the Amazon Bedrock User Guide.
Deletes a custom model that you created earlier. For more information, see Custom models in the Amazon Bedrock User Guide.
Deletes the account-level enforced guardrail configuration.
Deletes the account-level enforced guardrail configuration.
Delete the model access agreement for the specified model.
Delete the model access agreement for the specified model.
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.
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.
Deletes a custom model that you imported earlier. For more information, see Import a customized model in the Amazon Bedrock User Guide.
Deletes a custom model that you imported earlier. For more information, see Import a customized model in the Amazon Bedrock User Guide.
Deletes an application inference profile. For more information, see Increase throughput and resilience with cross-region inference in Amazon Bedrock. in the Amazon Bedrock User Guide.
Deletes an application inference profile. For more information, see Increase throughput and resilience with cross-region inference in Amazon Bedrock. in the Amazon Bedrock User Guide.
Deletes an endpoint for a model from Amazon Bedrock Marketplace.
Deletes an endpoint for a model from Amazon Bedrock Marketplace.
Delete the invocation logging.
Delete the invocation logging.
Deletes a specified prompt router. This action cannot be undone.
Deletes a specified prompt router. This action cannot be undone.
Deletes a Provisioned Throughput. You can't delete a Provisioned Throughput before the commitment term is over. For more information, see Provisioned Throughput in the Amazon Bedrock User Guide.
Deletes a Provisioned Throughput. You can't delete a Provisioned Throughput before the commitment term is over. For more information, see Provisioned Throughput in the Amazon Bedrock User Guide.
Deletes a previously created Bedrock resource policy.
Deletes a previously created Bedrock resource policy.
Deregisters an endpoint for a model from Amazon Bedrock Marketplace. This operation removes the endpoint's association with Amazon Bedrock but does not delete the underlying Amazon SageMaker endpoint.
Returned if the service cannot complete the request.
Deregisters an endpoint for a model from Amazon Bedrock Marketplace. This operation removes the endpoint's association with Amazon Bedrock but does not delete the underlying Amazon SageMaker endpoint.
Dimensional price rate.
A summary of the RAG resources used in an Amazon Bedrock Knowledge Base evaluation job. These resources can be Knowledge Bases in Amazon Bedrock or RAG sources outside of Amazon Bedrock that you use to generate your own inference response data.
A summary of the models used in an Amazon Bedrock model evaluation job. These resources can be models in Amazon Bedrock or models outside of Amazon Bedrock that you use to generate your own inference response data.
Identifies the models, Knowledge Bases, or other RAG sources evaluated in a model or Knowledge Base evaluation job.
Summary information of an evaluation job.
Exports the policy definition for an Automated Reasoning policy version. Returns the complete policy definition including rules, variables, and custom variable types in a structured format.
Exports the policy definition for an Automated Reasoning policy version. Returns the complete policy definition including rules, variables, and custom variable types in a structured format.
Details about whether a model version is available or deprecated.
Information about a foundation model.
Summary information for a foundation model.
Get Advanced Prompt Optimization Job Request
Get Advanced Prompt Optimization Job Response
Retrieves the current annotations for an Automated Reasoning policy build workflow. Annotations contain corrections to the rules, variables and types to be applied to the policy.
Retrieves the current annotations for an Automated Reasoning policy build workflow. Annotations contain corrections to the rules, variables and types to be applied to the policy.
Retrieves detailed information about an Automated Reasoning policy build workflow, including its status, configuration, and metadata.
Retrieves detailed information about an Automated Reasoning policy build workflow, including its status, configuration, and metadata.
Retrieves the resulting assets from a completed Automated Reasoning policy build workflow, including build logs, quality reports, and generated policy artifacts.
Retrieves the resulting assets from a completed Automated Reasoning policy build workflow, including build logs, quality reports, and generated policy artifacts.
Retrieves the next test scenario for validating an Automated Reasoning policy. This is used during the interactive policy refinement process to test policy behavior.
Retrieves the next test scenario for validating an Automated Reasoning policy. This is used during the interactive policy refinement process to test policy behavior.
Retrieves details about an Automated Reasoning policy or policy version. Returns information including the policy definition, metadata, and timestamps.
Retrieves details about an Automated Reasoning policy or policy version. Returns information including the policy definition, metadata, and timestamps.
Retrieves details about a specific Automated Reasoning policy test.
Retrieves details about a specific Automated Reasoning policy test.
Retrieves the test result for a specific Automated Reasoning policy test. Returns detailed validation findings and execution status.
Retrieves the test result for a specific Automated Reasoning policy test. Returns detailed validation findings and execution status.
Retrieves information about a custom model deployment, including its status, configuration, and metadata. Use this operation to monitor the deployment status and retrieve details needed for inference requests. The following actions are related to the GetCustomModelDeployment operation: CreateCustomModelDeployment ListCustomModelDeployments DeleteCustomModelDeployment
Retrieves information about a custom model deployment, including its status, configuration, and metadata. Use this operation to monitor the deployment status and retrieve details needed for inference requests. The following actions are related to the GetCustomModelDeployment operation: CreateCustomModelDeployment ListCustomModelDeployments DeleteCustomModelDeployment
Get the properties associated with a Amazon Bedrock custom model that you have created. For more information, see Custom models in the Amazon Bedrock User Guide.
The metric for the validator.
Metrics associated with the custom job.
Get the properties associated with a Amazon Bedrock custom model that you have created. For more information, see Custom models in the Amazon Bedrock User Guide.
Gets information about an evaluation job, such as the status of the job.
Gets information about an evaluation job, such as the status of the job.
Get information about the Foundation model availability.
Get information about the Foundation model availability.
Get details about a Amazon Bedrock foundation model.
Get details about a Amazon Bedrock foundation model.
Gets details about a guardrail. If you don't specify a version, the response returns details for the DRAFT version.
A word configured for the guardrail.
The managed word list that was configured for the guardrail. (This is a list of words that are pre-defined and managed by guardrails only.)
Contains details about the word policy configured for the guardrail.
The tier that your guardrail uses for denied topic filters.
Details about topics for the guardrail to identify and deny. This data type is used in the following API operations: GetGuardrail response body
Contains details about topics that the guardrail should identify and deny. This data type is used in the following API operations: GetGuardrail response body
The regular expression configured for the guardrail.
The PII entity configured for the guardrail.
Contains details about PII entities and regular expressions configured for the guardrail.
Contains details about the system-defined guardrail profile that you're using with your guardrail for cross-Region inference. For more information, see the Amazon Bedrock User Guide.
The details for the guardrails contextual grounding filter.
The details for the guardrails contextual grounding policy.
The tier that your guardrail uses for content filters.
Contains filter strengths for harmful content. Guardrails support the following content filters to detect and filter harmful user inputs and FM-generated outputs. Hate – Describes language or a statement that discriminates, criticizes, insults, denounces, or dehumanizes a person or group on the basis of an identity (such as race, ethnicity, gender, religion, sexual orientation, ability, and national origin). Insults – Describes language or a statement that includes demeaning, humiliating, mocking, insulting, or belittling language. This type of language is also labeled as bullying. Sexual – Describes language or a statement that indicates sexual interest, activity, or arousal using direct or indirect references to body parts, physical traits, or sex. Violence – Describes language or a statement that includes glorification of or threats to inflict physical pain, hurt, or injury toward a person, group or thing. Content filtering depends on the confidence classification of user inputs and FM responses across each of the four harmful categories. All input and output statements are classified into one of four confidence levels (NONE, LOW, MEDIUM, HIGH) for each harmful category. For example, if a statement is classified as Hate with HIGH confidence, the likelihood of the statement representing hateful content is high. A single statement can be classified across multiple categories with varying confidence levels. For example, a single statement can be classified as Hate with HIGH confidence, Insults with LOW confidence, Sexual with NONE confidence, and Violence with MEDIUM confidence. For more information, see Guardrails content filters. This data type is used in the following API operations: GetGuardrail response body
Contains details about how to handle harmful content. This data type is used in the following API operations: GetGuardrail response body
Represents the configuration of Automated Reasoning policies within a Amazon Bedrock Guardrail, including the policies to apply and confidence thresholds.
Gets details about a guardrail. If you don't specify a version, the response returns details for the DRAFT version.
Gets properties associated with a customized model you imported.
Gets properties associated with a customized model you imported.
Gets information about an inference profile. For more information, see Increase throughput and resilience with cross-region inference in Amazon Bedrock. in the Amazon Bedrock User Guide.
Contains information about a model.
Gets information about an inference profile. For more information, see Increase throughput and resilience with cross-region inference in Amazon Bedrock. in the Amazon Bedrock User Guide.
Retrieves details about a specific endpoint for a model from Amazon Bedrock Marketplace.
Retrieves details about a specific endpoint for a model from Amazon Bedrock Marketplace.
Retrieves information about a model copy job. For more information, see Copy models to be used in other regions in the Amazon Bedrock User Guide.
Retrieves information about a model copy job. For more information, see Copy models to be used in other regions in the Amazon Bedrock User Guide.
Retrieves the properties associated with a model-customization job, including the status of the job. For more information, see Custom models in the Amazon Bedrock User Guide.
For a Distillation job, the status details for the validation sub-task of the job.
For a Distillation job, the status details for the training sub-task of the job.
For a Distillation job, the status details for sub-tasks of the job. Possible statuses for each sub-task include the following: NotStarted InProgress Completed Stopping Stopped Failed
Retrieves the properties associated with a model-customization job, including the status of the job. For more information, see Custom models in the Amazon Bedrock User Guide.
Retrieves the properties associated with import model job, including the status of the job. For more information, see Import a customized model in the Amazon Bedrock User Guide.
Retrieves the properties associated with import model job, including the status of the job. For more information, see Import a customized model in the Amazon Bedrock User Guide.
Gets details about a batch inference job. For more information, see Monitor batch inference jobs
A non-negative long value.
Gets details about a batch inference job. For more information, see Monitor batch inference jobs
Get the current configuration values for model invocation logging.
Configuration fields for invocation logging.
Get the current configuration values for model invocation logging.
Retrieves details about a prompt router.
Retrieves details about a prompt router.
Returns details for a Provisioned Throughput. For more information, see Provisioned Throughput in the Amazon Bedrock User Guide.
Returns details for a Provisioned Throughput. For more information, see Provisioned Throughput in the Amazon Bedrock User Guide.
Gets the resource policy document for a Bedrock resource
Gets the resource policy document for a Bedrock resource
Get usecase for model access.
Get usecase for model access.
Contains details about a guardrail. This data type is used in the following API operations: ListGuardrails response body
Information about the imported model.
Contains information about an inference profile.
List Advanced Prompt Optimization Jobs Request
List Advanced Prompt Optimization Jobs Response
Lists all Automated Reasoning policies in your account, with optional filtering by policy ARN. This helps you manage and discover existing policies.
Lists all Automated Reasoning policies in your account, with optional filtering by policy ARN. This helps you manage and discover existing policies.
Lists all build workflows for an Automated Reasoning policy, showing the history of policy creation and modification attempts.
Lists all build workflows for an Automated Reasoning policy, showing the history of policy creation and modification attempts.
Lists tests for an Automated Reasoning policy. We recommend using pagination to ensure that the operation returns quickly and successfully.
Lists tests for an Automated Reasoning policy. We recommend using pagination to ensure that the operation returns quickly and successfully.
Lists test results for an Automated Reasoning policy, showing how the policy performed against various test scenarios and validation checks.
Lists test results for an Automated Reasoning policy, showing how the policy performed against various test scenarios and validation checks.
Lists custom model deployments in your account. You can filter the results by creation time, name, status, and associated model. Use this operation to manage and monitor your custom model deployments. We recommend using pagination to ensure that the operation returns quickly and successfully. The following actions are related to the ListCustomModelDeployments operation: CreateCustomModelDeployment GetCustomModelDeployment DeleteCustomModelDeployment
Lists custom model deployments in your account. You can filter the results by creation time, name, status, and associated model. Use this operation to manage and monitor your custom model deployments. We recommend using pagination to ensure that the operation returns quickly and successfully. The following actions are related to the ListCustomModelDeployments operation: CreateCustomModelDeployment GetCustomModelDeployment DeleteCustomModelDeployment
Returns a list of the custom models that you have created with the CreateModelCustomizationJob operation. For more information, see Custom models in the Amazon Bedrock User Guide.
Returns a list of the custom models that you have created with the CreateModelCustomizationJob operation. For more information, see Custom models in the Amazon Bedrock User Guide.
Lists the account-level enforced guardrail configurations.
Lists the account-level enforced guardrail configurations.
Lists all existing evaluation jobs.
Lists all existing evaluation jobs.
Get the offers associated with the specified model.
Describes the validity terms.
Describes a support term.
Describes the usage-based pricing term.
Describes the usage terms of an offer.
Get the offers associated with the specified model.
Lists Amazon Bedrock foundation models that you can use. You can filter the results with the request parameters. For more information, see Foundation models in the Amazon Bedrock User Guide.
Lists Amazon Bedrock foundation models that you can use. You can filter the results with the request parameters. For more information, see Foundation models in the Amazon Bedrock User Guide.
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.
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.
Returns a list of models you've imported. You can filter the results to return based on one or more criteria. For more information, see Import a customized model in the Amazon Bedrock User Guide.
Returns a list of models you've imported. You can filter the results to return based on one or more criteria. For more information, see Import a customized model in the Amazon Bedrock User Guide.
Returns a list of inference profiles that you can use. For more information, see Increase throughput and resilience with cross-region inference in Amazon Bedrock. in the Amazon Bedrock User Guide.
Returns a list of inference profiles that you can use. For more information, see Increase throughput and resilience with cross-region inference in Amazon Bedrock. in the Amazon Bedrock User Guide.
Lists the endpoints for models from Amazon Bedrock Marketplace in your Amazon Web Services account.
Provides a summary of an endpoint for a model from Amazon Bedrock Marketplace.
Lists the endpoints for models from Amazon Bedrock Marketplace in your Amazon Web Services account.
Returns a list of model copy jobs that you have submitted. You can filter the jobs to return based on one or more criteria. For more information, see Copy models to be used in other regions in the Amazon Bedrock User Guide.
Contains details about each model copy job. This data type is used in the following API operations: ListModelCopyJobs response
Returns a list of model copy jobs that you have submitted. You can filter the jobs to return based on one or more criteria. For more information, see Copy models to be used in other regions in the Amazon Bedrock User Guide.
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.
Information about one customization job
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.
Returns a list of import jobs you've submitted. You can filter the results to return based on one or more criteria. For more information, see Import a customized model in the Amazon Bedrock User Guide.
Information about the import job.
Returns a list of import jobs you've submitted. You can filter the results to return based on one or more criteria. For more information, see Import a customized model in the Amazon Bedrock User Guide.
Lists all batch inference jobs in the account. For more information, see View details about a batch inference job.
A summary of a batch inference job.
Lists all batch inference jobs in the account. For more information, see View details about a batch inference job.
Retrieves a list of prompt routers.
Details about a prompt router.
Retrieves a list of prompt routers.
Lists the Provisioned Throughputs in the account. For more information, see Provisioned Throughput in the Amazon Bedrock User Guide.
A summary of information about a Provisioned Throughput. This data type is used in the following API operations: ListProvisionedThroughputs response
Lists the Provisioned Throughputs in the account. For more information, see Provisioned Throughput in the Amazon Bedrock User Guide.
List the tags associated with the specified resource. For more information, see Tagging resources in the Amazon Bedrock User Guide.
List the tags associated with the specified resource. For more information, see Tagging resources in the Amazon Bedrock User Guide.
Sets the account-level enforced guardrail configuration.
Sets the account-level enforced guardrail configuration.
Set the configuration values for model invocation logging.
Set the configuration values for model invocation logging.
Adds a resource policy for a Bedrock resource.
Adds a resource policy for a Bedrock resource.
Put usecase for model access.
Put usecase for model access.
Registers an existing Amazon SageMaker endpoint with Amazon Bedrock Marketplace, allowing it to be used with Amazon Bedrock APIs.
Registers an existing Amazon SageMaker endpoint with Amazon Bedrock Marketplace, allowing it to be used with Amazon Bedrock APIs.
Starts a new build workflow for an Automated Reasoning policy. This initiates the process of analyzing source documents and generating policy rules, variables, and types.
Starts a new build workflow for an Automated Reasoning policy. This initiates the process of analyzing source documents and generating policy rules, variables, and types.
Initiates a test workflow to validate Automated Reasoning policy tests. The workflow executes the specified tests against the policy and generates validation results.
Initiates a test workflow to validate Automated Reasoning policy tests. The workflow executes the specified tests against the policy and generates validation results.
Stop Advanced Prompt Optimization Job Request
Stop Advanced Prompt Optimization Job Response
Stops an evaluation job that is current being created or running.
Stops an evaluation job that is current being created or running.
Stops an active model customization job. For more information, see Custom models in the Amazon Bedrock User Guide.
Stops an active model customization job. For more information, see Custom models in the Amazon Bedrock User Guide.
Stops a batch inference job. You're only charged for tokens that were already processed. For more information, see Stop a batch inference job.
Stops a batch inference job. You're only charged for tokens that were already processed. For more information, see Stop a batch inference job.
Associate tags with a resource. For more information, see Tagging resources in the Amazon Bedrock User Guide.
Associate tags with a resource. For more information, see Tagging resources in the Amazon Bedrock User Guide.
Remove one or more tags from a resource. For more information, see Tagging resources in the Amazon Bedrock User Guide.
Remove one or more tags from a resource. For more information, see Tagging resources in the Amazon Bedrock User Guide.
Updates the annotations for an Automated Reasoning policy build workflow. This allows you to modify extracted rules, variables, and types before finalizing the policy.
Updates the annotations for an Automated Reasoning policy build workflow. This allows you to modify extracted rules, variables, and types before finalizing the policy.
Updates an existing Automated Reasoning policy with new rules, variables, or configuration. This creates a new version of the policy while preserving the previous version.
Updates an existing Automated Reasoning policy with new rules, variables, or configuration. This creates a new version of the policy while preserving the previous version.
Updates an existing Automated Reasoning policy test. You can modify the content, query, expected result, and confidence threshold.
Updates an existing Automated Reasoning policy test. You can modify the content, query, expected result, and confidence threshold.
Updates a custom model deployment with a new custom model. This allows you to deploy updated models without creating new deployment endpoints.
Updates a custom model deployment with a new custom model. This allows you to deploy updated models without creating new deployment endpoints.
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.
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.
Updates the configuration of an existing endpoint for a model from Amazon Bedrock Marketplace.
Updates the configuration of an existing endpoint for a model from Amazon Bedrock Marketplace.
Updates the name or associated model for a Provisioned Throughput. For more information, see Provisioned Throughput in the Amazon Bedrock User Guide.
Updates the name or associated model for a Provisioned Throughput. For more information, see Provisioned Throughput in the Amazon Bedrock User Guide.