langchain.callbacks.clearml_callback.ClearMLCallbackHandler

class langchain.callbacks.clearml_callback.ClearMLCallbackHandler(task_type: Optional[str] = 'inference', project_name: Optional[str] = 'langchain_callback_demo', tags: Optional[Sequence] = None, task_name: Optional[str] = None, visualize: bool = False, complexity_metrics: bool = False, stream_logs: bool = False)

Bases: BaseMetadataCallbackHandler, BaseCallbackHandler

Callback Handler that logs to ClearML.

Parameters

• job_type (str) – The type of clearml task such as “inference”, “testing” or “qc”

• project_name (str) – The clearml project name

• tags (list) – Tags to add to the task

• task_name (str) – Name of the clearml task

• visualize (bool) – Whether to visualize the run.

• complexity_metrics (bool) – Whether to log complexity metrics

• stream_logs (bool) – Whether to stream callback actions to ClearML

This handler will utilize the associated callback method 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 the ClearML console.

Initialize callback handler.

Methods

__init__([task_type, project_name, tags, ...])	Initialize callback handler.
analyze_text(text)	Analyze text using textstat and spacy.
flush_tracker([name, langchain_asset, finish])	Flush the tracker and setup the session.
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_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.

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

analyze_text(text: str) → dict

Analyze text using textstat and spacy.

Parameters

text (str) – The text to analyze.

Returns

A dictionary containing the complexity metrics.

Return type

(dict)

flush_tracker(name: Optional[str] = None, langchain_asset: Any = None, finish: bool = False) → None

Flush the tracker and setup the session.

Everything after this will be a new table.

Parameters

• name – Name of the performed session so far so it is identifiable

• langchain_asset – The langchain asset to save.

• finish – Whether to finish the run.

• Returns – 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: Dict[str, Any], **kwargs: Any) → None

Run when chain ends running.

on_chain_error(error: Union[Exception, KeyboardInterrupt], **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: Union[Exception, KeyboardInterrupt], **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], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any

Run when Retriever ends running.

on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, 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_text(text: str, **kwargs: Any) → None

Run when agent is ending.

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

Run when tool ends running.

on_tool_error(error: Union[Exception, KeyboardInterrupt], **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.

property always_verbose: bool

Whether to call verbose callbacks even if verbose is False.

property ignore_agent: bool

Whether to ignore agent callbacks.

property ignore_chain: bool

Whether to ignore chain callbacks.

property ignore_chat_model: bool

Whether to ignore chat model callbacks.

property ignore_llm: bool

Whether to ignore LLM callbacks.

property ignore_retriever: bool

Whether to ignore retriever callbacks.

property ignore_retry: bool

Whether to ignore retry callbacks.

raise_error: bool = False

run_inline: bool = False

Examples using ClearMLCallbackHandler

• ClearML