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# `langchain_community.callbacks.sagemaker_callback`.SageMakerCallbackHandler

class langchain_community.callbacks.sagemaker_callback.SageMakerCallbackHandler(run: Any)

  Callback Handler that logs prompt artifacts and metrics to SageMaker Experiments.

  Parameters

    run (sagemaker.experiments.run.Run) – Run object where the experiment is logged.

  Initialize callback handler.

  Attributes

  `ignore_agent` | Whether to ignore agent callbacks.
  `ignore_chain` | Whether to ignore chain callbacks.
  `ignore_chat_model` | Whether to ignore chat model callbacks.
  `ignore_llm` | Whether to ignore LLM callbacks.
  `ignore_retriever` | Whether to ignore retriever callbacks.
  `ignore_retry` | Whether to ignore retry callbacks.
  `raise_error` |
  `run_inline` |

  Methods

  `__init__`(run) | Initialize callback handler.
  `flush_tracker`() | Reset the steps and delete the temporary local directory.
  `jsonf`(data, data_dir, filename[, is_output]) | To log the input data as json file artifact.
  `on_agent_action`(action, **kwargs) | Run on agent action.
  `on_agent_finish`(finish, **kwargs) | Run when agent ends running.
  `on_chain_end`(outputs, **kwargs) | Run when chain ends running.
  `on_chain_error`(error, **kwargs) | Run when chain errors.
  `on_chain_start`(serialized, inputs, **kwargs) | Run when chain starts running.
  `on_chat_model_start`(serialized, messages, *, ...) | Run when a chat model starts running.
  `on_llm_end`(response, **kwargs) | Run when LLM ends running.
  `on_llm_error`(error, **kwargs) | Run when LLM errors.
  `on_llm_new_token`(token, **kwargs) | Run when LLM generates a new token.
  `on_llm_start`(serialized, prompts, **kwargs) | Run when LLM starts.
  `on_retriever_end`(documents, *, run_id[, ...]) | Run when Retriever ends running.
  `on_retriever_error`(error, *, run_id[, ...]) | Run when Retriever errors.
  `on_retriever_start`(serialized, query, *, run_id) | Run when Retriever starts running.
  `on_retry`(retry_state, *, run_id[, parent_run_id]) | Run on a retry event.
  `on_text`(text, **kwargs) | Run when agent is ending.
  `on_tool_end`(output, **kwargs) | Run when tool ends running.
  `on_tool_error`(error, **kwargs) | Run when tool errors.
  `on_tool_start`(serialized, input_str, **kwargs) | Run when tool starts running.

  __init__(run: Any) → None

    Initialize callback handler.

    Parameters

      run (Any) –

    Return type

      None

  flush_tracker() → None

    Reset the steps and delete the temporary local directory.

    Return type

      None

  jsonf(data: Dict[str, Any], data_dir: str, filename: str, is_output: Optional[bool] = True) → None

    To log the input data as json file artifact.

    Parameters

      - data (Dict[str, Any]) –

      - data_dir (str) –

      - filename (str) –

      - is_output (Optional[bool]) –

    Return type

      None

  on_agent_action(action: AgentAction, **kwargs: Any) → Any

    Run on agent action.

    Parameters

      - action (AgentAction) –

      - kwargs (Any) –

    Return type

      Any

  on_agent_finish(finish: AgentFinish, **kwargs: Any) → None

    Run when agent ends running.

    Parameters

      - finish (AgentFinish) –

      - kwargs (Any) –

    Return type

      None

  on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None

    Run when chain ends running.

    Parameters

      - outputs (Dict[str, Any]) –

      - kwargs (Any) –

    Return type

      None

  on_chain_error(error: BaseException, **kwargs: Any) → None

    Run when chain errors.

    Parameters

      - error (BaseException) –

      - kwargs (Any) –

    Return type

      None

  on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) → None

    Run when chain starts running.

    Parameters

      - serialized (Dict[str, Any]) –

      - inputs (Dict[str, Any]) –

      - kwargs (Any) –

    Return type

      None

  on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any

    Run when a chat model starts running.

    ATTENTION: This method is called for chat models. If you’re implementing

      a handler for a non-chat model, you should use on_llm_start instead.

    Parameters

      - serialized (Dict[str, Any]) –

      - messages (List[List[BaseMessage]]) –

      - run_id (UUID) –

      - parent_run_id (Optional[UUID]) –

      - tags (Optional[List[str]]) –

      - metadata (Optional[Dict[str, Any]]) –

      - kwargs (Any) –

    Return type

      Any

  on_llm_end(response: LLMResult, **kwargs: Any) → None

    Run when LLM ends running.

    Parameters

      - response (LLMResult) –

      - kwargs (Any) –

    Return type

      None

  on_llm_error(error: BaseException, **kwargs: Any) → None

    Run when LLM errors.

    Parameters

      - error (BaseException) –

      - kwargs (Any) –

    Return type

      None

  on_llm_new_token(token: str, **kwargs: Any) → None

    Run when LLM generates a new token.

    Parameters

      - token (str) –

      - kwargs (Any) –

    Return type

      None

  on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None

    Run when LLM starts.

    Parameters

      - serialized (Dict[str, Any]) –

      - prompts (List[str]) –

      - kwargs (Any) –

    Return type

      None

  on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any

    Run when Retriever ends running.

    Parameters

      - documents (Sequence[Document]) –

      - run_id (UUID) –

      - parent_run_id (Optional[UUID]) –

      - kwargs (Any) –

    Return type

      Any

  on_retriever_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any

    Run when Retriever errors.

    Parameters

      - error (BaseException) –

      - run_id (UUID) –

      - parent_run_id (Optional[UUID]) –

      - kwargs (Any) –

    Return type

      Any

  on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any

    Run when Retriever starts running.

    Parameters

      - serialized (Dict[str, Any]) –

      - query (str) –

      - run_id (UUID) –

      - parent_run_id (Optional[UUID]) –

      - tags (Optional[List[str]]) –

      - metadata (Optional[Dict[str, Any]]) –

      - kwargs (Any) –

    Return type

      Any

  on_retry(retry_state: RetryCallState, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any

    Run on a retry event.

    Parameters

      - retry_state (RetryCallState) –

      - run_id (UUID) –

      - parent_run_id (Optional[UUID]) –

      - kwargs (Any) –

    Return type

      Any

  on_text(text: str, **kwargs: Any) → None

    Run when agent is ending.

    Parameters

      - text (str) –

      - kwargs (Any) –

    Return type

      None

  on_tool_end(output: Any, **kwargs: Any) → None

    Run when tool ends running.

    Parameters

      - output (Any) –

      - kwargs (Any) –

    Return type

      None

  on_tool_error(error: BaseException, **kwargs: Any) → None

    Run when tool errors.

    Parameters

      - error (BaseException) –

      - kwargs (Any) –

    Return type

      None

  on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None

    Run when tool starts running.

    Parameters

      - serialized (Dict[str, Any]) –

      - input_str (str) –

      - kwargs (Any) –

    Return type

      None

# Examples using SageMakerCallbackHandler

- AWS

- LLM Hyperparameters