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 = '')[source]

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.

  • run_id (Optional[str]) –

  • artifacts_dir (str) –

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[source]

Initialize callback handler.

Parameters
  • name (Optional[str]) –

  • experiment (Optional[str]) –

  • tags (Optional[Dict]) –

  • tracking_uri (Optional[str]) –

  • run_id (Optional[str]) –

  • artifacts_dir (str) –

Return type

None

flush_tracker(langchain_asset: Optional[Any] = None, finish: bool = False) None[source]
Parameters
  • langchain_asset (Optional[Any]) –

  • finish (bool) –

Return type

None

get_custom_callback_meta() Dict[str, Any]
Return type

Dict[str, Any]

on_agent_action(action: AgentAction, **kwargs: Any) Any[source]

Run on agent action.

Parameters
Return type

Any

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

Run when agent ends running.

Parameters
Return type

None

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

Run when chain ends running.

Parameters
  • outputs (Union[Dict[str, Any], str, List[str]]) –

  • kwargs (Any) –

Return type

None

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

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[source]

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[source]

Run when LLM ends running.

Parameters
Return type

None

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

Run when LLM errors.

Parameters
  • error (BaseException) –

  • kwargs (Any) –

Return type

None

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

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[source]

Run when LLM starts.

Parameters
  • serialized (Dict[str, Any]) –

  • prompts (List[str]) –

  • kwargs (Any) –

Return type

None

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

Run when Retriever ends running.

Parameters
  • documents (Sequence[Document]) –

  • kwargs (Any) –

Return type

Any

on_retriever_error(error: BaseException, **kwargs: Any) Any[source]

Run when Retriever errors.

Parameters
  • error (BaseException) –

  • kwargs (Any) –

Return type

Any

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

Run when Retriever starts running.

Parameters
  • serialized (Dict[str, Any]) –

  • query (str) –

  • 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[source]

Run when text is received.

Parameters
  • text (str) –

  • kwargs (Any) –

Return type

None

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

Run when tool ends running.

Parameters
  • output (Any) –

  • kwargs (Any) –

Return type

None

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

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[source]

Run when tool starts running.

Parameters
  • serialized (Dict[str, Any]) –

  • input_str (str) –

  • kwargs (Any) –

Return type

None

reset_callback_meta() None

Reset the callback metadata.

Return type

None

Examples using MlflowCallbackHandler