strands.models.sagemaker

Amazon SageMaker model provider.

UsageMetadata

@dataclass
class UsageMetadata()

Defined in: src/strands/models/sagemaker.py:28

Usage metadata for the model.

Attributes:

• total_tokens - Total number of tokens used in the request
• completion_tokens - Number of tokens used in the completion
• prompt_tokens - Number of tokens used in the prompt
• prompt_tokens_details - Additional information about the prompt tokens (optional)

FunctionCall

@dataclass
class FunctionCall()

Defined in: src/strands/models/sagemaker.py:45

Function call for the model.

Attributes:

• name - Name of the function to call
• arguments - Arguments to pass to the function

__init__

def __init__(**kwargs: dict[str, str])

Defined in: src/strands/models/sagemaker.py:56

Initialize function call.

Arguments:

• **kwargs - Keyword arguments for the function call.

ToolCall

@dataclass
class ToolCall()

Defined in: src/strands/models/sagemaker.py:67

Tool call for the model object.

Attributes:

• id - Tool call ID
• type - Tool call type
• function - Tool call function

__init__

def __init__(**kwargs: dict)

Defined in: src/strands/models/sagemaker.py:80

Initialize tool call object.

Arguments:

• **kwargs - Keyword arguments for the tool call.

SageMakerAIModel

class SageMakerAIModel(OpenAIModel)

Defined in: src/strands/models/sagemaker.py:91

Amazon SageMaker model provider implementation.

client

type: ignore[assignment]

SageMakerAIPayloadSchema

class SageMakerAIPayloadSchema(TypedDict)

Defined in: src/strands/models/sagemaker.py:96

Payload schema for the Amazon SageMaker AI model.

Attributes:

• max_tokens - Maximum number of tokens to generate in the completion
• stream - Whether to stream the response
• temperature - Sampling temperature to use for the model (optional)
• top_p - Nucleus sampling parameter (optional)
• top_k - Top-k sampling parameter (optional)
• stop - List of stop sequences to use for the model (optional)
• tool_results_as_user_messages - Convert tool result to user messages (optional)
• additional_args - Additional request parameters, as supported by https://bit.ly/djl-lmi-request-schema

SageMakerAIEndpointConfig

class SageMakerAIEndpointConfig(TypedDict)

Defined in: src/strands/models/sagemaker.py:119

Configuration options for SageMaker models.

Attributes:

• endpoint_name - The name of the SageMaker endpoint to invoke

• inference_component_name - The name of the inference component to use

• additional_args - Other request parameters, as supported by https://bit.ly/sagemaker-invoke-endpoint-params

__init__

def __init__(endpoint_config: SageMakerAIEndpointConfig,
             payload_config: SageMakerAIPayloadSchema,
             boto_session: boto3.Session | None = None,
             boto_client_config: BotocoreConfig | None = None)

Defined in: src/strands/models/sagemaker.py:136

Initialize provider instance.

Arguments:

• endpoint_config - Endpoint configuration for SageMaker.
• payload_config - Payload configuration for the model.
• boto_session - Boto Session to use when calling the SageMaker Runtime.
• boto_client_config - Configuration to use when creating the SageMaker-Runtime Boto Client.

update_config

@override
def update_config(**endpoint_config: Unpack[SageMakerAIEndpointConfig]
                  ) -> None

Defined in: src/strands/models/sagemaker.py:181

Update the Amazon SageMaker model configuration with the provided arguments.

Arguments:

• **endpoint_config - Configuration overrides.

get_config

@override
def get_config() -> "SageMakerAIModel.SageMakerAIEndpointConfig"

Defined in: src/strands/models/sagemaker.py:191

Get the Amazon SageMaker model configuration.

Returns:

The Amazon SageMaker model configuration.

format_request

@override
def format_request(messages: Messages,
                   tool_specs: list[ToolSpec] | None = None,
                   system_prompt: str | None = None,
                   tool_choice: ToolChoice | None = None,
                   **kwargs: Any) -> dict[str, Any]

Defined in: src/strands/models/sagemaker.py:200

Format an Amazon SageMaker chat streaming request.

Arguments:

• messages - List of message objects to be processed by the model.
• tool_specs - List of tool specifications to make available to the model.
• system_prompt - System prompt to provide context to the model.
• tool_choice - Selection strategy for tool invocation. Note: This parameter is accepted for interface consistency but is currently ignored for this model provider.
• **kwargs - Additional keyword arguments for future extensibility.

Returns:

An Amazon SageMaker chat streaming request.

stream

@override
async def stream(messages: Messages,
                 tool_specs: list[ToolSpec] | None = None,
                 system_prompt: str | None = None,
                 *,
                 tool_choice: ToolChoice | None = None,
                 **kwargs: Any) -> AsyncGenerator[StreamEvent, None]

Defined in: src/strands/models/sagemaker.py:301

Stream conversation with the SageMaker model.

Arguments:

• messages - List of message objects to be processed by the model.
• tool_specs - List of tool specifications to make available to the model.
• system_prompt - System prompt to provide context to the model.
• tool_choice - Selection strategy for tool invocation. Note: This parameter is accepted for interface consistency but is currently ignored for this model provider.
• **kwargs - Additional keyword arguments for future extensibility.

Yields:

Formatted message chunks from the model.

format_request_tool_message

@override
@classmethod
def format_request_tool_message(cls, tool_result: ToolResult,
                                **kwargs: Any) -> dict[str, Any]

Defined in: src/strands/models/sagemaker.py:507

Format a SageMaker compatible tool message.

Arguments:

• tool_result - Tool result collected from a tool execution.
• **kwargs - Additional keyword arguments for future extensibility.

Returns:

SageMaker compatible tool message with content as a string.

format_request_message_content

@override
@classmethod
def format_request_message_content(cls, content: ContentBlock,
                                   **kwargs: Any) -> dict[str, Any]

Defined in: src/strands/models/sagemaker.py:538

Format a content block.

Arguments:

• content - Message content.
• **kwargs - Additional keyword arguments for future extensibility.

Returns:

Formatted content block.

Raises:

• TypeError - If the content block type cannot be converted to a SageMaker-compatible format.

structured_output

@override
async def structured_output(
        output_model: type[T],
        prompt: Messages,
        system_prompt: str | None = None,
        **kwargs: Any) -> AsyncGenerator[dict[str, T | Any], None]

Defined in: src/strands/models/sagemaker.py:575

Get structured output from the model.

Arguments:

• output_model - The output model to use for the agent.
• prompt - The prompt messages to use for the agent.
• system_prompt - System prompt to provide context to the model.
• **kwargs - Additional keyword arguments for future extensibility.

Yields:

Model events with the last being the structured output.