create_agent ¶

def create_agent(
    self,
    agent_name: "capo_bedrock_agent.types.name.Name",
    *,
    config_overrides: Optional[BedrockAgentClientConfig] = None,
    client_token: Optional[
        "capo_bedrock_agent.types.client_token.ClientToken"
    ] = 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,
    agent_resource_role_arn: Optional[
        "capo_bedrock_agent.types.agent_role_arn.AgentRoleArn"
    ] = None,
    customer_encryption_key_arn: Optional[
        "capo_bedrock_agent.types.kms_key_arn.KmsKeyArn"
    ] = None,
    tags: Optional["capo_bedrock_agent.types.tags_map.TagsMap"] = 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.create_agent_response.CreateAgentResponse":
    """<note> <p>Amazon Bedrock Agents (now Amazon Bedrock Agents Classic) is no longer open to new customers. For capabilities similar to Bedrock Agents Classic, explore Amazon Bedrock AgentCore. Existing customers can continue to use the service as normal. For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/agents-classic-maintenance-mode.html">Amazon Bedrock Agents Classic availability change</a>.</p> </note> <p>Creates an agent that orchestrates interactions between foundation models, data sources, software applications, user conversations, and APIs to carry out tasks to help customers.</p> <ul> <li> <p>Specify the following fields for security purposes.</p> <ul> <li> <p> <code>agentResourceRoleArn</code> – The Amazon Resource Name (ARN) of the role with permissions to invoke API operations on an agent.</p> </li> <li> <p>(Optional) <code>customerEncryptionKeyArn</code> – The Amazon Resource Name (ARN) of a KMS key to encrypt the creation of the agent.</p> </li> <li> <p>(Optional) <code>idleSessionTTLinSeconds</code> – Specify the number of seconds for which the agent should maintain session information. After this time expires, the subsequent <code>InvokeAgent</code> request begins a new session.</p> </li> </ul> </li> <li> <p>To enable your agent to retain conversational context across multiple sessions, include a <code>memoryConfiguration</code> object. For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/agents-configure-memory.html">Configure memory</a>.</p> </li> <li> <p>To override the default prompt behavior for agent orchestration and to use advanced prompts, include a <code>promptOverrideConfiguration</code> object. For more information, see <a href="https://docs.aws.amazon.com/bedrock/latest/userguide/advanced-prompts.html">Advanced prompts</a>.</p> </li> <li> <p>If your agent fails to be created, the response returns a list of <code>failureReasons</code> alongside a list of <code>recommendedActions</code> for you to troubleshoot.</p> </li> <li> <p>The agent instructions will not be honored if your agent has only one knowledge base, uses default prompts, has no action group, and user input is disabled.</p> </li> </ul>

    Args:
        agent_name: <p>A name for the agent that you create.</p>
        client_token: <p>A unique, case-sensitive identifier to ensure that the API request completes no more than one time. 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>.</p>
        instruction: <p>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>A 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>
        tags: <p>Any tags that you want to attach to 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 created.</p>
        memory_configuration: <p> Contains the details of the memory configured 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.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.create_agent_request.CreateAgentRequest]",
    ) -> OperationResponse[
        "capo_bedrock_agent.types.create_agent_response.CreateAgentResponse"
    ]:
        import capo_bedrock_agent._operations.amazon_bedrock_agent_build_time_lambda.create_agent

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

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

        return capo_bedrock_agent.types.create_agent_request.redact_sensitive(
            input_
        )

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

        return capo_bedrock_agent.types.create_agent_response.redact_sensitive(
            output
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging(
            "CreateAgent", redact_input=_redact_input, redact_output=_redact_output
        ),
    )
    input_: capo_bedrock_agent.types.create_agent_request.CreateAgentRequest = {
        "agent_name": agent_name
    }
    if client_token is None:
        client_token = str(uuid.uuid4())
    input_["client_token"] = client_token
    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 agent_resource_role_arn is not None:
        input_["agent_resource_role_arn"] = agent_resource_role_arn
    if customer_encryption_key_arn is not None:
        input_["customer_encryption_key_arn"] = customer_encryption_key_arn
    if tags is not None:
        input_["tags"] = tags
    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