langchain_community.callbacks.mlflow_callback.MlflowCallbackHandler

class langchain_community.callbacks.mlflow_callback.MlflowCallbackHandler(name: Optional[str] = 'langchainrun-%', experiment: Optional[str] = 'langchain', tags: Optional[Dict] = None, tracking_uri: Optional[str] = None, run_id: Optional[str] = None, artifacts_dir: str = '')

Callback Handler that logs metrics and artifacts to mlflow server.

Parameters

• name (str) – Name of the run.

• experiment (str) – Name of the experiment.

• tags (dict) – Tags to be attached for the run.

• tracking_uri (str) – MLflow tracking server uri.

This handler will utilize the associated callback method called and formats the input of each callback function with metadata regarding the state of LLM run, and adds the response to the list of records for both the {method}_records and action. It then logs the response to mlflow server.

Initialize callback handler.

Attributes

always_verbose	Whether to call verbose callbacks even if verbose is False.
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__([name, experiment, tags, ...])	Initialize callback handler.
flush_tracker([langchain_asset, finish])
get_custom_callback_meta()
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, **kwargs)	Run when Retriever ends running.
on_retriever_error(error, **kwargs)	Run when Retriever errors.
on_retriever_start(serialized, query, **kwargs)	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 text is received.
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.
reset_callback_meta()	Reset the callback metadata.

__init__(name: Optional[str] = 'langchainrun-%', experiment: Optional[str] = 'langchain', tags: Optional[Dict] = None, tracking_uri: Optional[str] = None, run_id: Optional[str] = None, artifacts_dir: str = '') → None

Initialize callback handler.

flush_tracker(langchain_asset: Any = None, finish: bool = False) → None

get_custom_callback_meta() → Dict[str, Any]

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

Run on agent action.

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

Run when agent ends running.

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

Run when chain ends running.

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

Run when chain errors.

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

Run when chain starts running.

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.

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

Run when LLM ends running.

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

Run when LLM errors.

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

Run when LLM generates a new token.

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

Run when LLM starts.

on_retriever_end(documents: Sequence[Document], **kwargs: Any) → Any

Run when Retriever ends running.

on_retriever_error(error: BaseException, **kwargs: Any) → Any

Run when Retriever errors.

on_retriever_start(serialized: Dict[str, Any], query: str, **kwargs: Any) → Any

Run when Retriever starts running.

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

Run on a retry event.

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

Run when text is received.

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

Run when tool ends running.

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

Run when tool errors.

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

Run when tool starts running.

reset_callback_meta() → None

Reset the callback metadata.

Examples using MlflowCallbackHandler

• MLflow