create_guardrail ¶

def create_guardrail(
    self,
    name: "capo_bedrock.types.guardrail_name.GuardrailName",
    blocked_input_messaging: "capo_bedrock.types.guardrail_blocked_messaging.GuardrailBlockedMessaging",
    blocked_outputs_messaging: "capo_bedrock.types.guardrail_blocked_messaging.GuardrailBlockedMessaging",
    *,
    config_overrides: Optional[BedrockClientConfig] = None,
    description: Optional[
        "capo_bedrock.types.guardrail_description.GuardrailDescription"
    ] = None,
    topic_policy_config: Optional[
        "capo_bedrock.types.guardrail_topic_policy_config.GuardrailTopicPolicyConfig"
    ] = None,
    content_policy_config: Optional[
        "capo_bedrock.types.guardrail_content_policy_config.GuardrailContentPolicyConfig"
    ] = None,
    word_policy_config: Optional[
        "capo_bedrock.types.guardrail_word_policy_config.GuardrailWordPolicyConfig"
    ] = None,
    sensitive_information_policy_config: Optional[
        "capo_bedrock.types.guardrail_sensitive_information_policy_config.GuardrailSensitiveInformationPolicyConfig"
    ] = None,
    contextual_grounding_policy_config: Optional[
        "capo_bedrock.types.guardrail_contextual_grounding_policy_config.GuardrailContextualGroundingPolicyConfig"
    ] = None,
    automated_reasoning_policy_config: Optional[
        "capo_bedrock.types.guardrail_automated_reasoning_policy_config.GuardrailAutomatedReasoningPolicyConfig"
    ] = None,
    cross_region_config: Optional[
        "capo_bedrock.types.guardrail_cross_region_config.GuardrailCrossRegionConfig"
    ] = None,
    kms_key_id: Optional["capo_bedrock.types.kms_key_id.KmsKeyId"] = None,
    tags: Optional["capo_bedrock.types.tag_list.TagList"] = None,
    client_request_token: Optional[
        "capo_bedrock.types.idempotency_token.IdempotencyToken"
    ] = None,
) -> "capo_bedrock.types.create_guardrail_response.CreateGuardrailResponse":
    """<p>Creates a guardrail to block topics and to implement safeguards for your generative AI applications.</p> <p>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.</p> <ul> <li> <p> <b>Content filters</b> - Adjust filter strengths to block input prompts or model responses containing harmful content.</p> </li> <li> <p> <b>Denied topics</b> - 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.</p> </li> <li> <p> <b>Word filters</b> - Configure filters to block undesirable words, phrases, and profanity. Such words can include offensive terms, competitor names etc.</p> </li> <li> <p> <b>Sensitive information filters</b> - Block or mask sensitive information such as personally identifiable information (PII) or custom regex in user inputs and model responses.</p> </li> </ul> <p>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.</p> <p>For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails.html">Amazon Bedrock Guardrails</a> in the <i>Amazon Bedrock User Guide</i>.</p>

    Args:
        name: <p>The name to give the guardrail.</p>
        description: <p>A description of the guardrail.</p>
        topic_policy_config: <p>The topic policies to configure for the guardrail.</p>
        content_policy_config: <p>The content filter policies to configure for the guardrail.</p>
        word_policy_config: <p>The word policy you configure for the guardrail.</p>
        sensitive_information_policy_config: <p>The sensitive information policy to configure for the guardrail.</p>
        contextual_grounding_policy_config: <p>The contextual grounding policy configuration used to create a guardrail.</p>
        automated_reasoning_policy_config: <p>Optional configuration for integrating Automated Reasoning policies with the new guardrail.</p>
        cross_region_config: <p>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.</p> <p>For more information, see the <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/guardrails-cross-region.html">Amazon Bedrock User Guide</a>.</p>
        blocked_input_messaging: <p>The message to return when the guardrail blocks a prompt.</p>
        blocked_outputs_messaging: <p>The message to return when the guardrail blocks a model response.</p>
        kms_key_id: <p>The ARN of the KMS key that you use to encrypt the guardrail.</p>
        tags: <p>The tags that you want to attach to the guardrail. </p>
        client_request_token: <p>A unique, case-sensitive identifier to ensure that the API request completes no more than once. If this token matches a previous request, Amazon Bedrock ignores the request, but does not return an error. For more information, see <a href="https://docs.aws.amazon.com/AWSEC2/latest/APIReference/Run_Instance_Idempotency.html">Ensuring idempotency</a> in the <i>Amazon S3 User Guide</i>.</p>

    Raises:
        capo_bedrock.errors.access_denied_exception.AccessDeniedException: <p>The request is denied because of missing access permissions.</p>
        capo_bedrock.errors.conflict_exception.ConflictException: <p>Error occurred because of a conflict while performing an operation.</p>
        capo_bedrock.errors.internal_server_exception.InternalServerException: <p>An internal server error occurred. Retry your request.</p>
        capo_bedrock.errors.resource_not_found_exception.ResourceNotFoundException: <p>The specified resource Amazon Resource Name (ARN) was not found. Check the Amazon Resource Name (ARN) and try your request again.</p>
        capo_bedrock.errors.service_quota_exceeded_exception.ServiceQuotaExceededException: <p>The number of requests exceeds the service quota. Resubmit your request later.</p>
        capo_bedrock.errors.throttling_exception.ThrottlingException: <p>The number of requests exceeds the limit. Resubmit your request later.</p>
        capo_bedrock.errors.too_many_tags_exception.TooManyTagsException: <p>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. </p>
        capo_bedrock.errors.validation_exception.ValidationException: <p>Input validation failed. Check your request parameters and retry the request.</p>
        capo_bedrock.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_bedrock.types.create_guardrail_request.CreateGuardrailRequest]",
    ) -> OperationResponse[
        "capo_bedrock.types.create_guardrail_response.CreateGuardrailResponse"
    ]:
        import capo_bedrock._operations.amazon_bedrock_control_plane_service.create_guardrail

        output, http_response = (
            capo_bedrock._operations.amazon_bedrock_control_plane_service.create_guardrail.create_guardrail(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    def _redact_input(
        input_: "capo_bedrock.types.create_guardrail_request.CreateGuardrailRequest",
    ) -> dict[str, Any]:
        import capo_bedrock.types.create_guardrail_request

        return capo_bedrock.types.create_guardrail_request.redact_sensitive(input_)

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging("CreateGuardrail", redact_input=_redact_input),
    )
    input_: capo_bedrock.types.create_guardrail_request.CreateGuardrailRequest = {
        "name": name,
        "blocked_input_messaging": blocked_input_messaging,
        "blocked_outputs_messaging": blocked_outputs_messaging,
    }
    if description is not None:
        input_["description"] = description
    if topic_policy_config is not None:
        input_["topic_policy_config"] = topic_policy_config
    if content_policy_config is not None:
        input_["content_policy_config"] = content_policy_config
    if word_policy_config is not None:
        input_["word_policy_config"] = word_policy_config
    if sensitive_information_policy_config is not None:
        input_["sensitive_information_policy_config"] = (
            sensitive_information_policy_config
        )
    if contextual_grounding_policy_config is not None:
        input_["contextual_grounding_policy_config"] = (
            contextual_grounding_policy_config
        )
    if automated_reasoning_policy_config is not None:
        input_["automated_reasoning_policy_config"] = (
            automated_reasoning_policy_config
        )
    if cross_region_config is not None:
        input_["cross_region_config"] = cross_region_config
    if kms_key_id is not None:
        input_["kms_key_id"] = kms_key_id
    if tags is not None:
        input_["tags"] = tags
    if client_request_token is None:
        client_request_token = str(uuid.uuid4())
    input_["client_request_token"] = client_request_token

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output