langchain.callbacks.mlflow_callback.MlflowCallbackHandler¶

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

Callback Handler that logs metrics and artifacts to mlflow server.

Parameters
  • name (str) ‚Äď Name of the run.

  • experiment (str) ‚Äď Name of the experiment.

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

  • tracking_uri (str) ‚Äď 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, tracking_uri])

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, *, 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.

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) None[source]¶

Initialize callback handler.

flush_tracker(langchain_asset: Any = None, finish: bool = False) None[source]¶
get_custom_callback_meta() Dict[str, Any]¶
on_agent_action(action: AgentAction, **kwargs: Any) Any[source]¶

Run on agent action.

on_agent_finish(finish: AgentFinish, **kwargs: Any) None[source]¶

Run when agent ends running.

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

Run when chain ends running.

on_chain_error(error: BaseException, **kwargs: Any) None[source]¶

Run when chain errors.

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

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[source]¶

Run when LLM ends running.

on_llm_error(error: BaseException, **kwargs: Any) None[source]¶

Run when LLM errors.

on_llm_new_token(token: str, **kwargs: Any) None[source]¶

Run when LLM generates a new token.

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

Run when LLM starts.

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

Run when Retriever ends running.

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

Run when Retriever errors.

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.

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[source]¶

Run when agent is ending.

on_tool_end(output: str, **kwargs: Any) None[source]¶

Run when tool ends running.

on_tool_error(error: BaseException, **kwargs: Any) None[source]¶

Run when tool errors.

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

Run when tool starts running.

reset_callback_meta() None¶

Reset the callback metadata.

Examples using MlflowCallbackHandler¶