update_knowledge_base ¶

def update_knowledge_base(
    self,
    knowledge_base_id: "capo_bedrock_agent.types.id.Id",
    name: "capo_bedrock_agent.types.name.Name",
    role_arn: "capo_bedrock_agent.types.knowledge_base_role_arn.KnowledgeBaseRoleArn",
    knowledge_base_configuration: "capo_bedrock_agent.types.knowledge_base_configuration.KnowledgeBaseConfiguration",
    *,
    config_overrides: Optional[BedrockAgentClientConfig] = None,
    description: Optional[
        "capo_bedrock_agent.types.description.Description"
    ] = None,
    storage_configuration: Optional[
        "capo_bedrock_agent.types.storage_configuration.StorageConfiguration"
    ] = None,
) -> "capo_bedrock_agent.types.update_knowledge_base_response.UpdateKnowledgeBaseResponse":
    """<p>Updates the configuration of a knowledge base with the fields that you specify. Because all fields will be overwritten, you must include the same values for fields that you want to keep the same.</p> <p>You can change the following fields:</p> <ul> <li> <p> <code>name</code> </p> </li> <li> <p> <code>description</code> </p> </li> <li> <p> <code>roleArn</code> </p> </li> </ul> <p>You can't change the <code>knowledgeBaseConfiguration</code> or <code>storageConfiguration</code> fields, so you must specify the same configurations as when you created the knowledge base. You can send a <a href="https://docs.aws.amazon.com/bedrock/latest/APIReference/API_agent_GetKnowledgeBase.html">GetKnowledgeBase</a> request and copy the same configurations.</p>

    Args:
        knowledge_base_id: <p>The unique identifier of the knowledge base to update.</p>
        name: <p>Specifies a new name for the knowledge base.</p>
        description: <p>Specifies a new description for the knowledge base.</p>
        role_arn: <p>Specifies a different Amazon Resource Name (ARN) of the IAM role with permissions to invoke API operations on the knowledge base.</p>
        knowledge_base_configuration: <p>Specifies the configuration for the embeddings model used for the knowledge base. You must use the same configuration as when the knowledge base was created.</p>
        storage_configuration: <p>Specifies the configuration for the vector store used for the knowledge base. You must use the same configuration as when the knowledge base was created.</p>

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

    def _handler(
        req: "OperationRequest[capo_bedrock_agent.types.update_knowledge_base_request.UpdateKnowledgeBaseRequest]",
    ) -> OperationResponse[
        "capo_bedrock_agent.types.update_knowledge_base_response.UpdateKnowledgeBaseResponse"
    ]:
        import capo_bedrock_agent._operations.amazon_bedrock_agent_build_time_lambda.update_knowledge_base

        output, http_response = (
            capo_bedrock_agent._operations.amazon_bedrock_agent_build_time_lambda.update_knowledge_base.update_knowledge_base(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    def _redact_input(
        input_: "capo_bedrock_agent.types.update_knowledge_base_request.UpdateKnowledgeBaseRequest",
    ) -> dict[str, Any]:
        import capo_bedrock_agent.types.update_knowledge_base_request

        return (
            capo_bedrock_agent.types.update_knowledge_base_request.redact_sensitive(
                input_
            )
        )

    def _redact_output(
        output: "capo_bedrock_agent.types.update_knowledge_base_response.UpdateKnowledgeBaseResponse",
    ) -> dict[str, Any]:
        import capo_bedrock_agent.types.update_knowledge_base_response

        return capo_bedrock_agent.types.update_knowledge_base_response.redact_sensitive(
            output
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging(
            "UpdateKnowledgeBase",
            redact_input=_redact_input,
            redact_output=_redact_output,
        ),
    )
    input_: capo_bedrock_agent.types.update_knowledge_base_request.UpdateKnowledgeBaseRequest = {
        "knowledge_base_id": knowledge_base_id,
        "name": name,
        "role_arn": role_arn,
        "knowledge_base_configuration": knowledge_base_configuration,
    }
    if description is not None:
        input_["description"] = description
    if storage_configuration is not None:
        input_["storage_configuration"] = storage_configuration

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