update_agent ¶

def update_agent(
    self,
    agent_id: "capo_bedrock_agent.types.id.Id",
    agent_name: "capo_bedrock_agent.types.name.Name",
    agent_resource_role_arn: "capo_bedrock_agent.types.agent_role_arn.AgentRoleArn",
    *,
    config_overrides: Optional[BedrockAgentClientConfig] = None,
    instruction: Optional[
        "capo_bedrock_agent.types.instruction.Instruction"
    ] = None,
    foundation_model: Optional[
        "capo_bedrock_agent.types.model_identifier.ModelIdentifier"
    ] = None,
    description: Optional[
        "capo_bedrock_agent.types.description.Description"
    ] = None,
    orchestration_type: Optional[
        "capo_bedrock_agent.types.orchestration_type.OrchestrationType"
    ] = None,
    custom_orchestration: Optional[
        "capo_bedrock_agent.types.custom_orchestration.CustomOrchestration"
    ] = None,
    idle_session_ttl_in_seconds: Optional[
        "capo_bedrock_agent.types.session_ttl.SessionTTL"
    ] = None,
    customer_encryption_key_arn: Optional[
        "capo_bedrock_agent.types.kms_key_arn.KmsKeyArn"
    ] = None,
    prompt_override_configuration: Optional[
        "capo_bedrock_agent.types.prompt_override_configuration.PromptOverrideConfiguration"
    ] = None,
    guardrail_configuration: Optional[
        "capo_bedrock_agent.types.guardrail_configuration.GuardrailConfiguration"
    ] = None,
    memory_configuration: Optional[
        "capo_bedrock_agent.types.memory_configuration.MemoryConfiguration"
    ] = None,
    agent_collaboration: Optional[
        "capo_bedrock_agent.types.agent_collaboration.AgentCollaboration"
    ] = None,
) -> "capo_bedrock_agent.types.update_agent_response.UpdateAgentResponse":
    """<p>Updates the configuration of an agent.</p>

    Args:
        agent_id: <p>The unique identifier of the agent.</p>
        agent_name: <p>Specifies a new name for the agent.</p>
        instruction: <p>Specifies new instructions that tell the agent what it should do and how it should interact with users.</p>
        foundation_model: <p>The identifier for the model that you want to be used for orchestration by the agent you create.</p> <p>The <code>modelId</code> to provide depends on the type of model or throughput that you use:</p> <ul> <li> <p>If you use a base model, specify the model ID or its ARN. For a list of model IDs for base models, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html#model-ids-arns">Amazon Bedrock base model IDs (on-demand throughput)</a> in the Amazon Bedrock User Guide.</p> </li> <li> <p>If you use an inference profile, specify the inference profile ID or its ARN. For a list of inference profile IDs, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference-support.html">Supported Regions and models for cross-region inference</a> in the Amazon Bedrock User Guide.</p> </li> <li> <p>If you use a provisioned model, specify the ARN of the Provisioned Throughput. For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/prov-thru-use.html">Run inference using a Provisioned Throughput</a> in the Amazon Bedrock User Guide.</p> </li> <li> <p>If you use a custom model, first purchase Provisioned Throughput for it. Then specify the ARN of the resulting provisioned model. For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-use.html">Use a custom model in Amazon Bedrock</a> in the Amazon Bedrock User Guide.</p> </li> <li> <p>If you use an <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/model-customization-import-model.html">imported model</a>, specify the ARN of the imported model. You can get the model ARN from a successful call to <a href="https://docs.aws.amazon.com/bedrock/latest/APIReference/API_CreateModelImportJob.html">CreateModelImportJob</a> or from the Imported models page in the Amazon Bedrock console.</p> </li> </ul>
        description: <p>Specifies a new description of the agent.</p>
        orchestration_type: <p> Specifies the type of orchestration strategy for the agent. This is set to <code>DEFAULT</code> orchestration type, by default. </p>
        custom_orchestration: <p> Contains details of the custom orchestration configured for the agent. </p>
        idle_session_ttl_in_seconds: <p>The number of seconds for which Amazon Bedrock keeps information about a user's conversation with the agent.</p> <p>A user interaction remains active for the amount of time specified. If no conversation occurs during this time, the session expires and Amazon Bedrock deletes any data provided before the timeout.</p>
        agent_resource_role_arn: <p>The Amazon Resource Name (ARN) of the IAM role with permissions to invoke API operations on the agent.</p>
        customer_encryption_key_arn: <p>The Amazon Resource Name (ARN) of the KMS key with which to encrypt the agent.</p>
        prompt_override_configuration: <p>Contains configurations to override prompts in different parts of an agent sequence. For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/advanced-prompts.html">Advanced prompts</a>.</p>
        guardrail_configuration: <p>The unique Guardrail configuration assigned to the agent when it is updated.</p>
        memory_configuration: <p>Specifies the new memory configuration for the agent. </p>
        agent_collaboration: <p>The agent's collaboration role.</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.service_quota_exceeded_exception.ServiceQuotaExceededException: <p>The number of requests exceeds the service quota. Resubmit your request later.</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_agent_request.UpdateAgentRequest]",
    ) -> OperationResponse[
        "capo_bedrock_agent.types.update_agent_response.UpdateAgentResponse"
    ]:
        import capo_bedrock_agent._operations.amazon_bedrock_agent_build_time_lambda.update_agent

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

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

        return capo_bedrock_agent.types.update_agent_request.redact_sensitive(
            input_
        )

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

        return capo_bedrock_agent.types.update_agent_response.redact_sensitive(
            output
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging(
            "UpdateAgent", redact_input=_redact_input, redact_output=_redact_output
        ),
    )
    input_: capo_bedrock_agent.types.update_agent_request.UpdateAgentRequest = {
        "agent_id": agent_id,
        "agent_name": agent_name,
        "agent_resource_role_arn": agent_resource_role_arn,
    }
    if instruction is not None:
        input_["instruction"] = instruction
    if foundation_model is not None:
        input_["foundation_model"] = foundation_model
    if description is not None:
        input_["description"] = description
    if orchestration_type is not None:
        input_["orchestration_type"] = orchestration_type
    if custom_orchestration is not None:
        input_["custom_orchestration"] = custom_orchestration
    if idle_session_ttl_in_seconds is not None:
        input_["idle_session_ttl_in_seconds"] = idle_session_ttl_in_seconds
    if customer_encryption_key_arn is not None:
        input_["customer_encryption_key_arn"] = customer_encryption_key_arn
    if prompt_override_configuration is not None:
        input_["prompt_override_configuration"] = prompt_override_configuration
    if guardrail_configuration is not None:
        input_["guardrail_configuration"] = guardrail_configuration
    if memory_configuration is not None:
        input_["memory_configuration"] = memory_configuration
    if agent_collaboration is not None:
        input_["agent_collaboration"] = agent_collaboration

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