langchain.callbacks.streamlit.streamlit_callback_handler.StreamlitCallbackHandler

class langchain.callbacks.streamlit.streamlit_callback_handler.StreamlitCallbackHandler(parent_container: DeltaGenerator, *, max_thought_containers: int = 4, expand_new_thoughts: bool = True, collapse_completed_thoughts: bool = True, thought_labeler: Optional[LLMThoughtLabeler] = None)[source]

A callback handler that writes to a Streamlit app.

Create a StreamlitCallbackHandler instance.

Parameters
  • parent_container – The st.container that will contain all the Streamlit elements that the Handler creates.

  • max_thought_containers – The max number of completed LLM thought containers to show at once. When this threshold is reached, a new thought will cause the oldest thoughts to be collapsed into a “History” expander. Defaults to 4.

  • expand_new_thoughts – Each LLM “thought” gets its own st.expander. This param controls whether that expander is expanded by default. Defaults to True.

  • collapse_completed_thoughts – If True, LLM thought expanders will be collapsed when completed. Defaults to True.

  • thought_labeler – An optional custom LLMThoughtLabeler instance. If unspecified, the handler will use the default thought labeling logic. Defaults to None.

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__(parent_container, *[, ...])

Create a StreamlitCallbackHandler instance.

on_agent_action(action[, color])

Run on agent action.

on_agent_finish(finish[, color])

Run on agent end.

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 on new LLM token.

on_llm_start(serialized, prompts, **kwargs)

Run when LLM starts running.

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[, color, end])

Run on arbitrary text.

on_tool_end(output[, color, ...])

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__(parent_container: DeltaGenerator, *, max_thought_containers: int = 4, expand_new_thoughts: bool = True, collapse_completed_thoughts: bool = True, thought_labeler: Optional[LLMThoughtLabeler] = None)[source]

Create a StreamlitCallbackHandler instance.

Parameters
  • parent_container – The st.container that will contain all the Streamlit elements that the Handler creates.

  • max_thought_containers – The max number of completed LLM thought containers to show at once. When this threshold is reached, a new thought will cause the oldest thoughts to be collapsed into a “History” expander. Defaults to 4.

  • expand_new_thoughts – Each LLM “thought” gets its own st.expander. This param controls whether that expander is expanded by default. Defaults to True.

  • collapse_completed_thoughts – If True, LLM thought expanders will be collapsed when completed. Defaults to True.

  • thought_labeler – An optional custom LLMThoughtLabeler instance. If unspecified, the handler will use the default thought labeling logic. Defaults to None.

on_agent_action(action: AgentAction, color: Optional[str] = None, **kwargs: Any) Any[source]

Run on agent action.

on_agent_finish(finish: AgentFinish, color: Optional[str] = None, **kwargs: Any) None[source]

Run on agent end.

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 on new LLM token. Only available when streaming is enabled.

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

Run when LLM starts running.

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, color: Optional[str] = None, end: str = '', **kwargs: Any) None[source]

Run on arbitrary text.

on_tool_end(output: str, color: Optional[str] = None, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **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.

Examples using StreamlitCallbackHandler