check_ingested_document_acl ¶

def check_ingested_document_acl(
    self,
    knowledge_base_id: "capo_bedrock_agent_runtime.types.knowledge_base_identifier.KnowledgeBaseIdentifier",
    data_source_id: "capo_bedrock_agent_runtime.types.data_source_id.DataSourceId",
    document_id: "capo_bedrock_agent_runtime.types.document_id.DocumentId",
    user_context: "capo_bedrock_agent_runtime.types.user_context.UserContext",
    *,
    config_overrides: Optional[BedrockAgentRuntimeClientConfig] = None,
) -> "capo_bedrock_agent_runtime.types.check_ingested_document_acl_response.CheckIngestedDocumentAclResponse":
    """<p>Checks whether a user has access to a specific document by verifying against the ingested access control list (ACL) in a knowledge base. Use this operation to validate that document-level access control is working as expected after ingestion. To use this operation, you must have the <code>bedrock:CheckIngestedDocumentAcl</code> permission.</p>

    Args:
        knowledge_base_id: <p>The unique identifier of the knowledge base that contains the document.</p>
        data_source_id: <p>The unique identifier of the data source that contains the document.</p>
        document_id: <p>The unique identifier of the document to check access for.</p>
        user_context: <p>The context object containing identity information for access control filtering, including user ID and optional group memberships used to evaluate the document access control list (ACL).</p>

    Raises:
        capo_bedrock_agent_runtime.errors.access_denied_exception.AccessDeniedException: <p>The request is denied because of missing access permissions. Check your permissions and retry your request.</p>
        capo_bedrock_agent_runtime.errors.internal_server_exception.InternalServerException: <p>An internal server error occurred. Retry your request.</p>
        capo_bedrock_agent_runtime.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_agent_runtime.errors.throttling_exception.ThrottlingException: <p>The number of requests exceeds the limit. Resubmit your request later.</p>
        capo_bedrock_agent_runtime.errors.validation_exception.ValidationException: <p>Input validation failed. Check your request parameters and retry the request.</p>
        capo_bedrock_agent_runtime.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_bedrock_agent_runtime.types.check_ingested_document_acl_request.CheckIngestedDocumentAclRequest]",
    ) -> OperationResponse[
        "capo_bedrock_agent_runtime.types.check_ingested_document_acl_response.CheckIngestedDocumentAclResponse"
    ]:
        import capo_bedrock_agent_runtime._operations.amazon_bedrock_agent_run_time_service.check_ingested_document_acl

        output, http_response = (
            capo_bedrock_agent_runtime._operations.amazon_bedrock_agent_run_time_service.check_ingested_document_acl.check_ingested_document_acl(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    def _redact_input(
        input_: "capo_bedrock_agent_runtime.types.check_ingested_document_acl_request.CheckIngestedDocumentAclRequest",
    ) -> dict[str, Any]:
        import capo_bedrock_agent_runtime.types.check_ingested_document_acl_request

        return capo_bedrock_agent_runtime.types.check_ingested_document_acl_request.redact_sensitive(
            input_
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging("CheckIngestedDocumentAcl", redact_input=_redact_input),
    )
    input_: capo_bedrock_agent_runtime.types.check_ingested_document_acl_request.CheckIngestedDocumentAclRequest = {
        "knowledge_base_id": knowledge_base_id,
        "data_source_id": data_source_id,
        "document_id": document_id,
        "user_context": user_context,
    }

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