agentic_retrieve_stream ¶

@contextmanager
def agentic_retrieve_stream(
    self,
    messages: "capo_bedrock_agent_runtime.types.agentic_retrieve_messages.AgenticRetrieveMessages",
    retrievers: "capo_bedrock_agent_runtime.types.agentic_retrievers.AgenticRetrievers",
    agentic_retrieve_configuration: "capo_bedrock_agent_runtime.types.agentic_retrieve_configuration.AgenticRetrieveConfiguration",
    *,
    config_overrides: Optional[BedrockAgentRuntimeClientConfig] = None,
    policy_configuration: Optional[
        "capo_bedrock_agent_runtime.types.agentic_retrieve_policy_configuration.AgenticRetrievePolicyConfiguration"
    ] = None,
    next_token: Optional[
        "capo_bedrock_agent_runtime.types.next_token.NextToken"
    ] = None,
    user_context: Optional[
        "capo_bedrock_agent_runtime.types.user_context.UserContext"
    ] = None,
    memory_configuration: Optional[
        "capo_bedrock_agent_runtime.types.agentic_retrieve_memory_configuration.AgenticRetrieveMemoryConfiguration"
    ] = None,
    generate_response: Optional[bool] = None,
) -> "Generator[capo_bedrock_agent_runtime.types.agentic_retrieve_stream_response.AgenticRetrieveStreamResponse]":
    """<p>Retrieves information from one or more knowledge bases using an agentic approach. Agentic retrieval uses a foundation model to intelligently decompose complex queries into sub-queries and iteratively retrieve relevant information from your knowledge bases. This approach improves retrieval accuracy for complex, multi-step questions that a single retrieval pass might not fully address.</p> <p>The operation returns results through a stream that includes retrieval results, trace events for visibility into the process, and a generated response synthesized from the results by default, which can be turned off.</p>

    Args:
        messages: <p>The list of messages for the agentic retrieval conversation.</p>
        retrievers: <p>The list of retrievers to use for agentic retrieval.</p>
        agentic_retrieve_configuration: <p>Configuration settings for the agentic retrieval operation.</p>
        policy_configuration: <p>Policy configuration for guardrails and content filtering.</p>
        next_token: <p>Opaque continuation token for paginated results.</p>
        user_context: <p>Contains information about the user making the request. This is used for access control filtering to ensure that retrieval results only include documents the user is authorized to access.</p>
        memory_configuration: <p>The configuration for using an Amazon Bedrock AgentCore Memory resource with this retrieval.</p>
        generate_response: <p>Whether to generate a response based on the retrieved results.</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.bad_gateway_exception.BadGatewayException: <p>There was an issue with a dependency due to a server issue. Retry your request.</p>
        capo_bedrock_agent_runtime.errors.conflict_exception.ConflictException: <p>There was a conflict performing an operation. Resolve the conflict and retry your request.</p>
        capo_bedrock_agent_runtime.errors.dependency_failed_exception.DependencyFailedException: <p>There was an issue with a dependency. Check the resource configurations and retry the 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.service_quota_exceeded_exception.ServiceQuotaExceededException: <p>The number of requests exceeds the service quota. Resubmit your request later.</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.agentic_retrieve_stream_request.AgenticRetrieveStreamRequest]",
    ) -> OperationResponse[
        "capo_bedrock_agent_runtime.types.agentic_retrieve_stream_response.AgenticRetrieveStreamResponse"
    ]:
        import capo_bedrock_agent_runtime._operations.amazon_bedrock_agent_run_time_service.agentic_retrieve_stream

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

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

        return capo_bedrock_agent_runtime.types.agentic_retrieve_stream_request.redact_sensitive(
            input_
        )

    def _redact_output(
        output: "capo_bedrock_agent_runtime.types.agentic_retrieve_stream_response.AgenticRetrieveStreamResponse",
    ) -> dict[str, Any]:
        import capo_bedrock_agent_runtime.types.agentic_retrieve_stream_response

        return capo_bedrock_agent_runtime.types.agentic_retrieve_stream_response.redact_sensitive(
            output
        )

    interceptors_, options_ = self.operation_options(
        config_overrides,
        OperationLogging(
            "AgenticRetrieveStream",
            redact_input=_redact_input,
            redact_output=_redact_output,
        ),
    )
    input_: capo_bedrock_agent_runtime.types.agentic_retrieve_stream_request.AgenticRetrieveStreamRequest = {
        "messages": messages,
        "retrievers": retrievers,
        "agentic_retrieve_configuration": agentic_retrieve_configuration,
    }
    if policy_configuration is not None:
        input_["policy_configuration"] = policy_configuration
    if next_token is not None:
        input_["next_token"] = next_token
    if user_context is not None:
        input_["user_context"] = user_context
    if memory_configuration is not None:
        input_["memory_configuration"] = memory_configuration
    if generate_response is not None:
        input_["generate_response"] = generate_response

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    try:
        yield response.output
    finally:
        response.response.close()