create_index async ¶

async def create_index(
    self,
    name: "capo_kendra.types.index_name.IndexName",
    role_arn: "capo_kendra.types.role_arn.RoleArn",
    *,
    config_overrides: Optional[AsynckendraClientConfig] = None,
    edition: Optional["capo_kendra.types.index_edition.IndexEdition"] = None,
    server_side_encryption_configuration: Optional[
        "capo_kendra.types.server_side_encryption_configuration.ServerSideEncryptionConfiguration"
    ] = None,
    description: Optional["capo_kendra.types.description.Description"] = None,
    client_token: Optional[
        "capo_kendra.types.client_token_name.ClientTokenName"
    ] = None,
    tags: Optional["capo_kendra.types.tag_list.TagList"] = None,
    user_token_configurations: Optional[
        "capo_kendra.types.user_token_configuration_list.UserTokenConfigurationList"
    ] = None,
    user_context_policy: Optional[
        "capo_kendra.types.user_context_policy.UserContextPolicy"
    ] = None,
    user_group_resolution_configuration: Optional[
        "capo_kendra.types.user_group_resolution_configuration.UserGroupResolutionConfiguration"
    ] = None,
) -> "capo_kendra.types.create_index_response.CreateIndexResponse":
    """<p>Creates an Amazon Kendra index. Index creation is an asynchronous API. To determine if index creation has completed, check the <code>Status</code> field returned from a call to <code>DescribeIndex</code>. The <code>Status</code> field is set to <code>ACTIVE</code> when the index is ready to use.</p> <p>Once the index is active, you can index your documents using the <code>BatchPutDocument</code> API or using one of the supported <a href="https://docs.aws.amazon.com/kendra/latest/dg/data-sources.html">data sources</a>.</p> <p>For an example of creating an index and data source using the Python SDK, see <a href="https://docs.aws.amazon.com/kendra/latest/dg/gs-python.html">Getting started with Python SDK</a>. For an example of creating an index and data source using the Java SDK, see <a href="https://docs.aws.amazon.com/kendra/latest/dg/gs-java.html">Getting started with Java SDK</a>.</p>

    Args:
        name: <p>A name for the index.</p>
        edition: <p>The Amazon Kendra edition to use for the index. Choose <code>DEVELOPER_EDITION</code> for indexes intended for development, testing, or proof of concept. Use <code>ENTERPRISE_EDITION</code> for production. Use <code>GEN_AI_ENTERPRISE_EDITION</code> for creating generative AI applications. Once you set the edition for an index, it can't be changed. </p> <p>The <code>Edition</code> parameter is optional. If you don't supply a value, the default is <code>ENTERPRISE_EDITION</code>.</p> <p>For more information on quota limits for Gen AI Enterprise Edition, Enterprise Edition, and Developer Edition indices, see <a href="https://docs.aws.amazon.com/kendra/latest/dg/quotas.html">Quotas</a>.</p>
        role_arn: <p>The Amazon Resource Name (ARN) of an IAM role with permission to access your Amazon CloudWatch logs and metrics. For more information, see <a href="https://docs.aws.amazon.com/kendra/latest/dg/iam-roles.html">IAM access roles for Amazon Kendra</a>.</p>
        server_side_encryption_configuration: <p>The identifier of the KMS customer managed key (CMK) that's used to encrypt data indexed by Amazon Kendra. Amazon Kendra doesn't support asymmetric CMKs.</p>
        description: <p>A description for the index.</p>
        client_token: <p>A token that you provide to identify the request to create an index. Multiple calls to the <code>CreateIndex</code> API with the same client token will create only one index.</p>
        tags: <p>A list of key-value pairs that identify or categorize the index. You can also use tags to help control access to the index. Tag keys and values can consist of Unicode letters, digits, white space, and any of the following symbols: _ . : / = + - @.</p>
        user_token_configurations: <p>The user token configuration.</p> <important> <p>If you're using an Amazon Kendra Gen AI Enterprise Edition index and you try to use <code>UserTokenConfigurations</code> to configure user context policy, Amazon Kendra returns a <code>ValidationException</code> error.</p> </important>
        user_context_policy: <p>The user context policy.</p> <important> <p>If you're using an Amazon Kendra Gen AI Enterprise Edition index, you can only use <code>ATTRIBUTE_FILTER</code> to filter search results by user context. If you're using an Amazon Kendra Gen AI Enterprise Edition index and you try to use <code>USER_TOKEN</code> to configure user context policy, Amazon Kendra returns a <code>ValidationException</code> error.</p> </important> <dl> <dt>ATTRIBUTE_FILTER</dt> <dd> <p>All indexed content is searchable and displayable for all users. If you want to filter search results on user context, you can use the attribute filters of <code>_user_id</code> and <code>_group_ids</code> or you can provide user and group information in <code>UserContext</code>. </p> </dd> <dt>USER_TOKEN</dt> <dd> <p>Enables token-based user access control to filter search results on user context. All documents with no access control and all documents accessible to the user will be searchable and displayable. </p> </dd> </dl>
        user_group_resolution_configuration: <p>Gets users and groups from IAM Identity Center identity source. To configure this, see <a href="https://docs.aws.amazon.com/kendra/latest/dg/API_UserGroupResolutionConfiguration.html">UserGroupResolutionConfiguration</a>. This is useful for user context filtering, where search results are filtered based on the user or their group access to documents.</p> <important> <p>If you're using an Amazon Kendra Gen AI Enterprise Edition index, <code>UserGroupResolutionConfiguration</code> isn't supported.</p> </important>

    Raises:
        capo_kendra.errors.access_denied_exception.AccessDeniedException: <p>You don't have sufficient access to perform this action. Please ensure you have the required permission policies and user accounts and try again.</p>
        capo_kendra.errors.conflict_exception.ConflictException: <p>A conflict occurred with the request. Please fix any inconsistences with your resources and try again.</p>
        capo_kendra.errors.internal_server_exception.InternalServerException: <p>An issue occurred with the internal server used for your Amazon Kendra service. Please wait a few minutes and try again, or contact <a href="http://aws.amazon.com/contact-us/">Support</a> for help.</p>
        capo_kendra.errors.resource_already_exist_exception.ResourceAlreadyExistException: <p>The resource you want to use already exists. Please check you have provided the correct resource and try again.</p>
        capo_kendra.errors.service_quota_exceeded_exception.ServiceQuotaExceededException: <p>You have exceeded the set limits for your Amazon Kendra service. Please see <a href="https://docs.aws.amazon.com/kendra/latest/dg/quotas.html">Quotas</a> for more information, or contact <a href="http://aws.amazon.com/contact-us/">Support</a> to inquire about an increase of limits.</p>
        capo_kendra.errors.throttling_exception.ThrottlingException: <p>The request was denied due to request throttling. Please reduce the number of requests and try again.</p>
        capo_kendra.errors.validation_exception.ValidationException: <p>The input fails to satisfy the constraints set by the Amazon Kendra service. Please provide the correct input and try again.</p>
        capo_kendra.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    async def _handler(
        req: "AsyncOperationRequest[capo_kendra.types.create_index_request.CreateIndexRequest]",
    ) -> AsyncOperationResponse[
        "capo_kendra.types.create_index_response.CreateIndexResponse"
    ]:
        import capo_kendra._operations.aws_kendra_frontend_service.create_index

        (
            output,
            http_response,
        ) = await capo_kendra._operations.aws_kendra_frontend_service.create_index.async_create_index(
            req.options, req.input
        )
        return AsyncOperationResponse(output=output, response=http_response)

    def _redact_input(
        input_: "capo_kendra.types.create_index_request.CreateIndexRequest",
    ) -> dict[str, Any]:
        import capo_kendra.types.create_index_request

        return capo_kendra.types.create_index_request.redact_sensitive(input_)

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging("CreateIndex", redact_input=_redact_input),
    )
    input_: capo_kendra.types.create_index_request.CreateIndexRequest = {
        "name": name,
        "role_arn": role_arn,
    }
    if edition is not None:
        input_["edition"] = edition
    if server_side_encryption_configuration is not None:
        input_["server_side_encryption_configuration"] = (
            server_side_encryption_configuration
        )
    if description is not None:
        input_["description"] = description
    if client_token is None:
        client_token = str(uuid.uuid4())
    input_["client_token"] = client_token
    if tags is not None:
        input_["tags"] = tags
    if user_token_configurations is not None:
        input_["user_token_configurations"] = user_token_configurations
    if user_context_policy is not None:
        input_["user_context_policy"] = user_context_policy
    if user_group_resolution_configuration is not None:
        input_["user_group_resolution_configuration"] = (
            user_group_resolution_configuration
        )

    response = await aexecute_pipeline(
        AsyncOperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    await response.response.aclose()
    return response.output