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07ac9f540219-2
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ignore_llm
Whether to ignore LLM callbacks.
ignore_retriever
Whether to ignore retriever callbacks.
raise_error
run_inline
analyze_text(text: str) → dict[source]¶
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[source]¶
Flush the tracker and setup the session.
Everything after this will be a new table.
Parameters
name – Name of the preformed session so far so it is identifyable
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[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: Union[Exception, KeyboardInterrupt], **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, **kwargs: Any) → Any¶
Run when a chat model starts running.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.clearml_callback.ClearMLCallbackHandler.html
|
07ac9f540219-3
|
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: Union[Exception, KeyboardInterrupt], **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: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
on_retriever_start(query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
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: Union[Exception, KeyboardInterrupt], **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.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.clearml_callback.ClearMLCallbackHandler.html
|
07ac9f540219-4
|
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.
raise_error: bool = False¶
run_inline: bool = False¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.clearml_callback.ClearMLCallbackHandler.html
|
e9cdf09ace32-0
|
langchain.callbacks.mlflow_callback.MlflowCallbackHandler¶
class langchain.callbacks.mlflow_callback.MlflowCallbackHandler(name: Optional[str] = 'langchainrun-%', experiment: Optional[str] = 'langchain', tags: Optional[Dict] = {}, tracking_uri: Optional[str] = None)[source]¶
Bases: BaseMetadataCallbackHandler, BaseCallbackHandler
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.
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.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.mlflow_callback.MlflowCallbackHandler.html
|
e9cdf09ace32-1
|
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(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.
raise_error
run_inline
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.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.mlflow_callback.MlflowCallbackHandler.html
|
e9cdf09ace32-2
|
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: Union[Exception, KeyboardInterrupt], **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, **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: Union[Exception, KeyboardInterrupt], **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: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.mlflow_callback.MlflowCallbackHandler.html
|
e9cdf09ace32-3
|
Run when Retriever errors.
on_retriever_start(query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
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: Union[Exception, KeyboardInterrupt], **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.
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.
raise_error: bool = False¶
run_inline: bool = False¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.mlflow_callback.MlflowCallbackHandler.html
|
76414c97e1ed-0
|
langchain.callbacks.aim_callback.import_aim¶
langchain.callbacks.aim_callback.import_aim() → Any[source]¶
Import the aim python package and raise an error if it is not installed.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.aim_callback.import_aim.html
|
aae5f8eae465-0
|
langchain.callbacks.infino_callback.InfinoCallbackHandler¶
class langchain.callbacks.infino_callback.InfinoCallbackHandler(model_id: Optional[str] = None, model_version: Optional[str] = None, verbose: bool = False)[source]¶
Bases: BaseCallbackHandler
Callback Handler that logs to Infino.
Methods
__init__([model_id, model_version, verbose])
on_agent_action(action, **kwargs)
Do nothing when agent takes a specific action.
on_agent_finish(finish, **kwargs)
Do nothing.
on_chain_end(outputs, **kwargs)
Do nothing when LLM chain ends.
on_chain_error(error, **kwargs)
Need to log the error.
on_chain_start(serialized, inputs, **kwargs)
Do nothing when LLM chain starts.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, **kwargs)
Log the latency, error, token usage, and response to Infino.
on_llm_error(error, **kwargs)
Set the error flag.
on_llm_new_token(token, **kwargs)
Do nothing when a new token is generated.
on_llm_start(serialized, prompts, **kwargs)
Log the prompts to Infino, and set start time and error flag.
on_retriever_end(documents, *, run_id[, ...])
Run when Retriever ends running.
on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
on_retriever_start(query, *, run_id[, ...])
Run when Retriever starts running.
on_text(text, **kwargs)
Do nothing.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.infino_callback.InfinoCallbackHandler.html
|
aae5f8eae465-1
|
on_text(text, **kwargs)
Do nothing.
on_tool_end(output[, observation_prefix, ...])
Do nothing when tool ends.
on_tool_error(error, **kwargs)
Do nothing when tool outputs an error.
on_tool_start(serialized, input_str, **kwargs)
Do nothing when tool starts.
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.
raise_error
run_inline
on_agent_action(action: AgentAction, **kwargs: Any) → Any[source]¶
Do nothing when agent takes a specific action.
on_agent_finish(finish: AgentFinish, **kwargs: Any) → None[source]¶
Do nothing.
on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Do nothing when LLM chain ends.
on_chain_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Need to log the error.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Do nothing when LLM chain starts.
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, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
Log the latency, error, token usage, and response to Infino.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.infino_callback.InfinoCallbackHandler.html
|
aae5f8eae465-2
|
Log the latency, error, token usage, and response to Infino.
on_llm_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Set the error flag.
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Do nothing when a new token is generated.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
Log the prompts to Infino, and set start time and error flag.
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(query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
on_text(text: str, **kwargs: Any) → None[source]¶
Do nothing.
on_tool_end(output: str, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any) → None[source]¶
Do nothing when tool ends.
on_tool_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Do nothing when tool outputs an error.
on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None[source]¶
Do nothing when tool starts.
property ignore_agent: bool¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.infino_callback.InfinoCallbackHandler.html
|
aae5f8eae465-3
|
Do nothing when tool starts.
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.
raise_error: bool = False¶
run_inline: bool = False¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.infino_callback.InfinoCallbackHandler.html
|
66f065e7d661-0
|
langchain.callbacks.manager.CallbackManagerForChainRun¶
class langchain.callbacks.manager.CallbackManagerForChainRun(*, run_id: UUID, handlers: List[BaseCallbackHandler], inheritable_handlers: List[BaseCallbackHandler], parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, inheritable_tags: Optional[List[str]] = None)[source]¶
Bases: RunManager, ChainManagerMixin
Callback manager for chain run.
Initialize the run manager.
Parameters
run_id (UUID) – The ID of the run.
handlers (List[BaseCallbackHandler]) – The list of handlers.
inheritable_handlers (List[BaseCallbackHandler]) – The list of inheritable handlers.
parent_run_id (UUID, optional) – The ID of the parent run.
Defaults to None.
tags (Optional[List[str]]) – The list of tags.
inheritable_tags (Optional[List[str]]) – The list of inheritable tags.
Methods
__init__(*, run_id, handlers, ...[, ...])
Initialize the run manager.
get_child([tag])
Get a child callback manager.
get_noop_manager()
Return a manager that doesn't perform any operations.
on_agent_action(action, **kwargs)
Run when agent action is received.
on_agent_finish(finish, **kwargs)
Run when agent finish is received.
on_chain_end(outputs, **kwargs)
Run when chain ends running.
on_chain_error(error, **kwargs)
Run when chain errors.
on_text(text, **kwargs)
Run when text is received.
get_child(tag: Optional[str] = None) → CallbackManager[source]¶
Get a child callback manager.
Parameters
tag (str, optional) – The tag for the child callback manager.
Defaults to None.
Returns
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.CallbackManagerForChainRun.html
|
66f065e7d661-1
|
Defaults to None.
Returns
The child callback manager.
Return type
CallbackManager
classmethod get_noop_manager() → BRM¶
Return a manager that doesn’t perform any operations.
Returns
The noop manager.
Return type
BaseRunManager
on_agent_action(action: AgentAction, **kwargs: Any) → Any[source]¶
Run when agent action is received.
Parameters
action (AgentAction) – The agent action.
Returns
The result of the callback.
Return type
Any
on_agent_finish(finish: AgentFinish, **kwargs: Any) → Any[source]¶
Run when agent finish is received.
Parameters
finish (AgentFinish) – The agent finish.
Returns
The result of the callback.
Return type
Any
on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Run when chain ends running.
Parameters
outputs (Dict[str, Any]) – The outputs of the chain.
on_chain_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when chain errors.
Parameters
error (Exception or KeyboardInterrupt) – The error.
on_text(text: str, **kwargs: Any) → Any¶
Run when text is received.
Parameters
text (str) – The received text.
Returns
The result of the callback.
Return type
Any
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.CallbackManagerForChainRun.html
|
4ad09dd6dd3e-0
|
langchain.callbacks.utils.import_spacy¶
langchain.callbacks.utils.import_spacy() → Any[source]¶
Import the spacy python package and raise an error if it is not installed.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.utils.import_spacy.html
|
a2a2757effc7-0
|
langchain.callbacks.manager.CallbackManagerForRetrieverRun¶
class langchain.callbacks.manager.CallbackManagerForRetrieverRun(*, run_id: UUID, handlers: List[BaseCallbackHandler], inheritable_handlers: List[BaseCallbackHandler], parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, inheritable_tags: Optional[List[str]] = None)[source]¶
Bases: RunManager, RetrieverManagerMixin
Callback manager for retriever run.
Initialize the run manager.
Parameters
run_id (UUID) – The ID of the run.
handlers (List[BaseCallbackHandler]) – The list of handlers.
inheritable_handlers (List[BaseCallbackHandler]) – The list of inheritable handlers.
parent_run_id (UUID, optional) – The ID of the parent run.
Defaults to None.
tags (Optional[List[str]]) – The list of tags.
inheritable_tags (Optional[List[str]]) – The list of inheritable tags.
Methods
__init__(*, run_id, handlers, ...[, ...])
Initialize the run manager.
get_child([tag])
Get a child callback manager.
get_noop_manager()
Return a manager that doesn't perform any operations.
on_retriever_end(documents, **kwargs)
Run when retriever ends running.
on_retriever_error(error, **kwargs)
Run when retriever errors.
on_text(text, **kwargs)
Run when text is received.
get_child(tag: Optional[str] = None) → CallbackManager[source]¶
Get a child callback manager.
classmethod get_noop_manager() → BRM¶
Return a manager that doesn’t perform any operations.
Returns
The noop manager.
Return type
BaseRunManager
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.CallbackManagerForRetrieverRun.html
|
a2a2757effc7-1
|
Returns
The noop manager.
Return type
BaseRunManager
on_retriever_end(documents: Sequence[Document], **kwargs: Any) → None[source]¶
Run when retriever ends running.
on_retriever_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when retriever errors.
on_text(text: str, **kwargs: Any) → Any¶
Run when text is received.
Parameters
text (str) – The received text.
Returns
The result of the callback.
Return type
Any
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.CallbackManagerForRetrieverRun.html
|
2796b60d1945-0
|
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]¶
Bases: BaseCallbackHandler
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.
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, *, ...)
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streamlit.streamlit_callback_handler.StreamlitCallbackHandler.html
|
2796b60d1945-1
|
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(query, *, run_id[, ...])
Run when Retriever starts running.
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.
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.
raise_error
run_inline
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.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streamlit.streamlit_callback_handler.StreamlitCallbackHandler.html
|
2796b60d1945-2
|
Run on agent end.
on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Run when chain ends running.
on_chain_error(error: Union[Exception, KeyboardInterrupt], **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, **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: Union[Exception, KeyboardInterrupt], **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.
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: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streamlit.streamlit_callback_handler.StreamlitCallbackHandler.html
|
2796b60d1945-3
|
Run when Retriever errors.
on_retriever_start(query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
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: Union[Exception, KeyboardInterrupt], **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.
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.
raise_error: bool = False¶
run_inline: bool = False¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streamlit.streamlit_callback_handler.StreamlitCallbackHandler.html
|
751134f7f78f-0
|
langchain.callbacks.arize_callback.ArizeCallbackHandler¶
class langchain.callbacks.arize_callback.ArizeCallbackHandler(model_id: Optional[str] = None, model_version: Optional[str] = None, SPACE_KEY: Optional[str] = None, API_KEY: Optional[str] = None)[source]¶
Bases: BaseCallbackHandler
Callback Handler that logs to Arize.
Initialize callback handler.
Methods
__init__([model_id, model_version, ...])
Initialize callback handler.
on_agent_action(action, **kwargs)
Do nothing.
on_agent_finish(finish, **kwargs)
Run on agent end.
on_chain_end(outputs, **kwargs)
Do nothing.
on_chain_error(error, **kwargs)
Do nothing.
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)
Do nothing.
on_llm_new_token(token, **kwargs)
Do nothing.
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(query, *, run_id[, ...])
Run when Retriever starts running.
on_text(text, **kwargs)
Run on arbitrary text.
on_tool_end(output[, observation_prefix, ...])
Run when tool ends running.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.arize_callback.ArizeCallbackHandler.html
|
751134f7f78f-1
|
on_tool_end(output[, observation_prefix, ...])
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.
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.
raise_error
run_inline
on_agent_action(action: AgentAction, **kwargs: Any) → Any[source]¶
Do nothing.
on_agent_finish(finish: AgentFinish, **kwargs: Any) → None[source]¶
Run on agent end.
on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Do nothing.
on_chain_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Do nothing.
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, **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: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Do nothing.
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.arize_callback.ArizeCallbackHandler.html
|
751134f7f78f-2
|
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Do nothing.
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: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
on_retriever_start(query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
on_text(text: str, **kwargs: Any) → None[source]¶
Run on arbitrary text.
on_tool_end(output: str, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any) → None[source]¶
Run when tool ends running.
on_tool_error(error: Union[Exception, KeyboardInterrupt], **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.
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¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.arize_callback.ArizeCallbackHandler.html
|
751134f7f78f-3
|
Whether to ignore LLM callbacks.
property ignore_retriever: bool¶
Whether to ignore retriever callbacks.
raise_error: bool = False¶
run_inline: bool = False¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.arize_callback.ArizeCallbackHandler.html
|
bb7fc8bd0de8-0
|
langchain.callbacks.aim_callback.AimCallbackHandler¶
class langchain.callbacks.aim_callback.AimCallbackHandler(repo: Optional[str] = None, experiment_name: Optional[str] = None, system_tracking_interval: Optional[int] = 10, log_system_params: bool = True)[source]¶
Bases: BaseMetadataCallbackHandler, BaseCallbackHandler
Callback Handler that logs to Aim.
Parameters
repo (str, optional) – Aim repository path or Repo object to which
Run object is bound. If skipped, default Repo is used.
experiment_name (str, optional) – Sets Run’s experiment property.
‘default’ if not specified. Can be used later to query runs/sequences.
system_tracking_interval (int, optional) – Sets the tracking interval
in seconds for system usage metrics (CPU, Memory, etc.). Set to None
to disable system metrics tracking.
log_system_params (bool, optional) – Enable/Disable logging of system
params such as installed packages, git info, environment variables, etc.
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 then logs the response to Aim.
Initialize callback handler.
Methods
__init__([repo, experiment_name, ...])
Initialize callback handler.
flush_tracker([repo, experiment_name, ...])
Flush the tracker and reset 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.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.aim_callback.AimCallbackHandler.html
|
bb7fc8bd0de8-1
|
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(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.
setup(**kwargs)
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.
raise_error
run_inline
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.aim_callback.AimCallbackHandler.html
|
bb7fc8bd0de8-2
|
ignore_retriever
Whether to ignore retriever callbacks.
raise_error
run_inline
flush_tracker(repo: Optional[str] = None, experiment_name: Optional[str] = None, system_tracking_interval: Optional[int] = 10, log_system_params: bool = True, langchain_asset: Any = None, reset: bool = True, finish: bool = False) → None[source]¶
Flush the tracker and reset the session.
Parameters
repo (str, optional) – Aim repository path or Repo object to which
Run object is bound. If skipped, default Repo is used.
experiment_name (str, optional) – Sets Run’s experiment property.
‘default’ if not specified. Can be used later to query runs/sequences.
system_tracking_interval (int, optional) – Sets the tracking interval
in seconds for system usage metrics (CPU, Memory, etc.). Set to None
to disable system metrics tracking.
log_system_params (bool, optional) – Enable/Disable logging of system
params such as installed packages, git info, environment variables, etc.
langchain_asset – The langchain asset to save.
reset – Whether to reset the session.
finish – Whether to finish the run.
Returns – None
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: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when chain errors.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.aim_callback.AimCallbackHandler.html
|
bb7fc8bd0de8-3
|
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, **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: Union[Exception, KeyboardInterrupt], **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: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
on_retriever_start(query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
on_text(text: str, **kwargs: Any) → None[source]¶
Run when agent is ending.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.aim_callback.AimCallbackHandler.html
|
bb7fc8bd0de8-4
|
Run when agent is ending.
on_tool_end(output: str, **kwargs: Any) → None[source]¶
Run when tool ends running.
on_tool_error(error: Union[Exception, KeyboardInterrupt], **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.
setup(**kwargs: Any) → None[source]¶
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.
raise_error: bool = False¶
run_inline: bool = False¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.aim_callback.AimCallbackHandler.html
|
07d58e0fe480-0
|
langchain.callbacks.utils.flatten_dict¶
langchain.callbacks.utils.flatten_dict(nested_dict: Dict[str, Any], parent_key: str = '', sep: str = '_') → Dict[str, Any][source]¶
Flattens a nested dictionary into a flat dictionary.
Parameters
nested_dict (dict) – The nested dictionary to flatten.
parent_key (str) – The prefix to prepend to the keys of the flattened dict.
sep (str) – The separator to use between the parent key and the key of the
flattened dictionary.
Returns
A flat dictionary.
Return type
(dict)
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.utils.flatten_dict.html
|
33bae458232e-0
|
langchain.callbacks.tracers.schemas.ToolRun¶
class langchain.callbacks.tracers.schemas.ToolRun(*, uuid: str, parent_uuid: Optional[str] = None, start_time: datetime = None, end_time: datetime = None, extra: Optional[Dict[str, Any]] = None, execution_order: int, child_execution_order: int, serialized: Dict[str, Any], session_id: int, error: Optional[str] = None, tool_input: str, output: Optional[str] = None, action: str, child_llm_runs: List[LLMRun] = None, child_chain_runs: List[ChainRun] = None, child_tool_runs: List[ToolRun] = None)[source]¶
Bases: BaseRun
Class for ToolRun.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param action: str [Required]¶
param child_chain_runs: List[langchain.callbacks.tracers.schemas.ChainRun] [Optional]¶
param child_execution_order: int [Required]¶
param child_llm_runs: List[langchain.callbacks.tracers.schemas.LLMRun] [Optional]¶
param child_tool_runs: List[langchain.callbacks.tracers.schemas.ToolRun] [Optional]¶
param end_time: datetime.datetime [Optional]¶
param error: Optional[str] = None¶
param execution_order: int [Required]¶
param extra: Optional[Dict[str, Any]] = None¶
param output: Optional[str] = None¶
param parent_uuid: Optional[str] = None¶
param serialized: Dict[str, Any] [Required]¶
param session_id: int [Required]¶
param start_time: datetime.datetime [Optional]¶
param tool_input: str [Required]¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.schemas.ToolRun.html
|
33bae458232e-1
|
param tool_input: str [Required]¶
param uuid: str [Required]¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.schemas.ToolRun.html
|
6ffec934f2c8-0
|
langchain.callbacks.tracers.run_collector.RunCollectorCallbackHandler¶
class langchain.callbacks.tracers.run_collector.RunCollectorCallbackHandler(example_id: Optional[Union[UUID, str]] = None, **kwargs: Any)[source]¶
Bases: BaseTracer
A tracer that collects all nested runs in a list.
This tracer is useful for inspection and evaluation purposes.
Parameters
example_id (Optional[Union[UUID, str]], default=None) – The ID of the example being traced. It can be either a UUID or a string.
Initialize the RunCollectorCallbackHandler.
Parameters
example_id (Optional[Union[UUID, str]], default=None) – The ID of the example being traced. It can be either a UUID or a string.
Methods
__init__([example_id])
Initialize the RunCollectorCallbackHandler.
on_agent_action(action, *, run_id[, ...])
Run on agent action.
on_agent_finish(finish, *, run_id[, ...])
Run on agent end.
on_chain_end(outputs, *, run_id, **kwargs)
End a trace for a chain run.
on_chain_error(error, *, run_id, **kwargs)
Handle an error for a chain run.
on_chain_start(serialized, inputs, *, run_id)
Start a trace for a chain run.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, *, run_id, **kwargs)
End a trace for an LLM run.
on_llm_error(error, *, run_id, **kwargs)
Handle an error for an LLM run.
on_llm_new_token(token, *, run_id[, ...])
Run on new LLM token.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.run_collector.RunCollectorCallbackHandler.html
|
6ffec934f2c8-1
|
Run on new LLM token.
on_llm_start(serialized, prompts, *, run_id)
Start a trace for an LLM run.
on_retriever_end(documents, *, run_id, **kwargs)
Run when Retriever ends running.
on_retriever_error(error, *, run_id, **kwargs)
Run when Retriever errors.
on_retriever_start(query, *, run_id[, ...])
Run when Retriever starts running.
on_text(text, *, run_id[, parent_run_id])
Run on arbitrary text.
on_tool_end(output, *, run_id, **kwargs)
End a trace for a tool run.
on_tool_error(error, *, run_id, **kwargs)
Handle an error for a tool run.
on_tool_start(serialized, input_str, *, run_id)
Start a trace for a tool run.
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.
name
raise_error
run_inline
on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent action.
on_agent_finish(finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent end.
on_chain_end(outputs: Dict[str, Any], *, run_id: UUID, **kwargs: Any) → None¶
End a trace for a chain run.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.run_collector.RunCollectorCallbackHandler.html
|
6ffec934f2c8-2
|
End a trace for a chain run.
on_chain_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for a chain run.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None¶
Start a trace for a chain run.
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, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, *, run_id: UUID, **kwargs: Any) → None¶
End a trace for an LLM run.
on_llm_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for an LLM run.
on_llm_new_token(token: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on new LLM token. Only available when streaming is enabled.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None¶
Start a trace for an LLM run.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, **kwargs: Any) → None¶
Run when Retriever ends running.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.run_collector.RunCollectorCallbackHandler.html
|
6ffec934f2c8-3
|
Run when Retriever ends running.
on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Run when Retriever errors.
on_retriever_start(query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None¶
Run when Retriever starts running.
on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on arbitrary text.
on_tool_end(output: str, *, run_id: UUID, **kwargs: Any) → None¶
End a trace for a tool run.
on_tool_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for a tool run.
on_tool_start(serialized: Dict[str, Any], input_str: str, *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None¶
Start a trace for a tool run.
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.
name = 'run-collector_callback_handler'¶
raise_error: bool = False¶
run_inline: bool = False¶
run_map: Dict[str, Run]¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.run_collector.RunCollectorCallbackHandler.html
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02c6636a00c2-0
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langchain.callbacks.flyte_callback.import_flytekit¶
langchain.callbacks.flyte_callback.import_flytekit() → Tuple[flytekit, renderer][source]¶
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.flyte_callback.import_flytekit.html
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4e1045bcd14c-0
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langchain.callbacks.tracers.schemas.TracerSessionBase¶
class langchain.callbacks.tracers.schemas.TracerSessionBase(*, start_time: datetime = None, name: Optional[str] = None, extra: Optional[Dict[str, Any]] = None, tenant_id: UUID)[source]¶
Bases: TracerSessionV1Base
A creation class for TracerSession.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param extra: Optional[Dict[str, Any]] = None¶
param name: Optional[str] = None¶
param start_time: datetime.datetime [Optional]¶
param tenant_id: uuid.UUID [Required]¶
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.schemas.TracerSessionBase.html
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9fe09cacb882-0
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langchain.callbacks.manager.AsyncCallbackManagerForLLMRun¶
class langchain.callbacks.manager.AsyncCallbackManagerForLLMRun(*, run_id: UUID, handlers: List[BaseCallbackHandler], inheritable_handlers: List[BaseCallbackHandler], parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, inheritable_tags: Optional[List[str]] = None)[source]¶
Bases: AsyncRunManager, LLMManagerMixin
Async callback manager for LLM run.
Initialize the run manager.
Parameters
run_id (UUID) – The ID of the run.
handlers (List[BaseCallbackHandler]) – The list of handlers.
inheritable_handlers (List[BaseCallbackHandler]) – The list of inheritable handlers.
parent_run_id (UUID, optional) – The ID of the parent run.
Defaults to None.
tags (Optional[List[str]]) – The list of tags.
inheritable_tags (Optional[List[str]]) – The list of inheritable tags.
Methods
__init__(*, run_id, handlers, ...[, ...])
Initialize the run manager.
get_noop_manager()
Return a manager that doesn't perform any operations.
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_text(text, **kwargs)
Run when text is received.
classmethod get_noop_manager() → BRM¶
Return a manager that doesn’t perform any operations.
Returns
The noop manager.
Return type
BaseRunManager
async on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
Run when LLM ends running.
Parameters
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.AsyncCallbackManagerForLLMRun.html
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9fe09cacb882-1
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Run when LLM ends running.
Parameters
response (LLMResult) – The LLM result.
async on_llm_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when LLM errors.
Parameters
error (Exception or KeyboardInterrupt) – The error.
async on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Run when LLM generates a new token.
Parameters
token (str) – The new token.
async on_text(text: str, **kwargs: Any) → Any¶
Run when text is received.
Parameters
text (str) – The received text.
Returns
The result of the callback.
Return type
Any
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.AsyncCallbackManagerForLLMRun.html
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a3802d0f6984-0
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langchain.callbacks.manager.trace_as_chain_group¶
langchain.callbacks.manager.trace_as_chain_group(group_name: str, *, project_name: Optional[str] = None, example_id: Optional[Union[UUID, str]] = None, tags: Optional[List[str]] = None) → Generator[CallbackManager, None, None][source]¶
Get a callback manager for a chain group in a context manager.
Useful for grouping different calls together as a single run even if
they aren’t composed in a single chain.
Parameters
group_name (str) – The name of the chain group.
project_name (str, optional) – The name of the project.
Defaults to None.
example_id (str or UUID, optional) – The ID of the example.
Defaults to None.
tags (List[str], optional) – The inheritable tags to apply to all runs.
Defaults to None.
Returns
The callback manager for the chain group.
Return type
CallbackManager
Example
>>> with trace_as_chain_group("group_name") as manager:
... # Use the callback manager for the chain group
... llm.predict("Foo", callbacks=manager)
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.trace_as_chain_group.html
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4edd0a05b3cd-0
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langchain.callbacks.wandb_callback.construct_html_from_prompt_and_generation¶
langchain.callbacks.wandb_callback.construct_html_from_prompt_and_generation(prompt: str, generation: str) → Any[source]¶
Construct an html element from a prompt and a generation.
Parameters
prompt (str) – The prompt.
generation (str) – The generation.
Returns
The html element.
Return type
(wandb.Html)
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.wandb_callback.construct_html_from_prompt_and_generation.html
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6173284dab41-0
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langchain.callbacks.manager.AsyncCallbackManagerForChainRun¶
class langchain.callbacks.manager.AsyncCallbackManagerForChainRun(*, run_id: UUID, handlers: List[BaseCallbackHandler], inheritable_handlers: List[BaseCallbackHandler], parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, inheritable_tags: Optional[List[str]] = None)[source]¶
Bases: AsyncRunManager, ChainManagerMixin
Async callback manager for chain run.
Initialize the run manager.
Parameters
run_id (UUID) – The ID of the run.
handlers (List[BaseCallbackHandler]) – The list of handlers.
inheritable_handlers (List[BaseCallbackHandler]) – The list of inheritable handlers.
parent_run_id (UUID, optional) – The ID of the parent run.
Defaults to None.
tags (Optional[List[str]]) – The list of tags.
inheritable_tags (Optional[List[str]]) – The list of inheritable tags.
Methods
__init__(*, run_id, handlers, ...[, ...])
Initialize the run manager.
get_child([tag])
Get a child callback manager.
get_noop_manager()
Return a manager that doesn't perform any operations.
on_agent_action(action, **kwargs)
Run when agent action is received.
on_agent_finish(finish, **kwargs)
Run when agent finish is received.
on_chain_end(outputs, **kwargs)
Run when chain ends running.
on_chain_error(error, **kwargs)
Run when chain errors.
on_text(text, **kwargs)
Run when text is received.
get_child(tag: Optional[str] = None) → AsyncCallbackManager[source]¶
Get a child callback manager.
Parameters
tag (str, optional) – The tag for the child callback manager.
Defaults to None.
Returns
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.AsyncCallbackManagerForChainRun.html
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6173284dab41-1
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Defaults to None.
Returns
The child callback manager.
Return type
AsyncCallbackManager
classmethod get_noop_manager() → BRM¶
Return a manager that doesn’t perform any operations.
Returns
The noop manager.
Return type
BaseRunManager
async on_agent_action(action: AgentAction, **kwargs: Any) → Any[source]¶
Run when agent action is received.
Parameters
action (AgentAction) – The agent action.
Returns
The result of the callback.
Return type
Any
async on_agent_finish(finish: AgentFinish, **kwargs: Any) → Any[source]¶
Run when agent finish is received.
Parameters
finish (AgentFinish) – The agent finish.
Returns
The result of the callback.
Return type
Any
async on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Run when chain ends running.
Parameters
outputs (Dict[str, Any]) – The outputs of the chain.
async on_chain_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when chain errors.
Parameters
error (Exception or KeyboardInterrupt) – The error.
async on_text(text: str, **kwargs: Any) → Any¶
Run when text is received.
Parameters
text (str) – The received text.
Returns
The result of the callback.
Return type
Any
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.AsyncCallbackManagerForChainRun.html
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f5e261cdcb89-0
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langchain.callbacks.streaming_aiter_final_only.AsyncFinalIteratorCallbackHandler¶
class langchain.callbacks.streaming_aiter_final_only.AsyncFinalIteratorCallbackHandler(*, answer_prefix_tokens: Optional[List[str]] = None, strip_tokens: bool = True, stream_prefix: bool = False)[source]¶
Bases: AsyncIteratorCallbackHandler
Callback handler that returns an async iterator.
Only the final output of the agent will be iterated.
Instantiate AsyncFinalIteratorCallbackHandler.
Parameters
answer_prefix_tokens – Token sequence that prefixes the answer.
Default is [“Final”, “Answer”, “:”]
strip_tokens – Ignore white spaces and new lines when comparing
answer_prefix_tokens to last tokens? (to determine if answer has been
reached)
stream_prefix – Should answer prefix itself also be streamed?
Methods
__init__(*[, answer_prefix_tokens, ...])
Instantiate AsyncFinalIteratorCallbackHandler.
aiter()
append_to_last_tokens(token)
check_if_answer_reached()
on_agent_action(action, *, run_id[, ...])
Run on agent action.
on_agent_finish(finish, *, run_id[, ...])
Run on agent end.
on_chain_end(outputs, *, run_id[, parent_run_id])
Run when chain ends running.
on_chain_error(error, *, run_id[, parent_run_id])
Run when chain errors.
on_chain_start(serialized, inputs, *, run_id)
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)
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streaming_aiter_final_only.AsyncFinalIteratorCallbackHandler.html
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f5e261cdcb89-1
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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 on retriever end.
on_retriever_error(error, *, run_id[, ...])
Run on retriever error.
on_retriever_start(query, *, run_id[, ...])
Run on retriever start.
on_text(text, *, run_id[, parent_run_id])
Run on arbitrary text.
on_tool_end(output, *, run_id[, parent_run_id])
Run when tool ends running.
on_tool_error(error, *, run_id[, parent_run_id])
Run when tool errors.
on_tool_start(serialized, input_str, *, run_id)
Run when tool starts running.
Attributes
always_verbose
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.
raise_error
run_inline
async aiter() → AsyncIterator[str]¶
append_to_last_tokens(token: str) → None[source]¶
check_if_answer_reached() → bool[source]¶
async on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None¶
Run on agent action.
async on_agent_finish(finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streaming_aiter_final_only.AsyncFinalIteratorCallbackHandler.html
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f5e261cdcb89-2
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Run on agent end.
async on_chain_end(outputs: Dict[str, Any], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None¶
Run when chain ends running.
async on_chain_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None¶
Run when chain errors.
async on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None¶
Run when chain starts running.
async 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, **kwargs: Any) → Any¶
Run when a chat model starts running.
async on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
Run when LLM ends running.
async on_llm_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None¶
Run when LLM errors.
async on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Run on new LLM token. Only available when streaming is enabled.
async on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
Run when LLM starts running.
async on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None¶
Run on retriever end.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streaming_aiter_final_only.AsyncFinalIteratorCallbackHandler.html
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f5e261cdcb89-3
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Run on retriever end.
async on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None¶
Run on retriever error.
async on_retriever_start(query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None¶
Run on retriever start.
async on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None¶
Run on arbitrary text.
async on_tool_end(output: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None¶
Run when tool ends running.
async on_tool_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None¶
Run when tool errors.
async on_tool_start(serialized: Dict[str, Any], input_str: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None¶
Run when tool starts running.
property always_verbose: bool¶
done: asyncio.Event¶
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.
queue: asyncio.Queue[str]¶
raise_error: bool = False¶
run_inline: bool = False¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streaming_aiter_final_only.AsyncFinalIteratorCallbackHandler.html
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20a06951fafc-0
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langchain.callbacks.tracers.langchain.log_error_once¶
langchain.callbacks.tracers.langchain.log_error_once(method: str, exception: Exception) → None[source]¶
Log an error once.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.langchain.log_error_once.html
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1cea210bbab4-0
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langchain.callbacks.tracers.base.TracerException¶
class langchain.callbacks.tracers.base.TracerException[source]¶
Bases: Exception
Base class for exceptions in tracers module.
add_note()¶
Exception.add_note(note) –
add a note to the exception
with_traceback()¶
Exception.with_traceback(tb) –
set self.__traceback__ to tb and return self.
args¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.base.TracerException.html
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e63bed603f73-0
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langchain.callbacks.tracers.schemas.ChainRun¶
class langchain.callbacks.tracers.schemas.ChainRun(*, uuid: str, parent_uuid: Optional[str] = None, start_time: datetime = None, end_time: datetime = None, extra: Optional[Dict[str, Any]] = None, execution_order: int, child_execution_order: int, serialized: Dict[str, Any], session_id: int, error: Optional[str] = None, inputs: Dict[str, Any], outputs: Optional[Dict[str, Any]] = None, child_llm_runs: List[LLMRun] = None, child_chain_runs: List[ChainRun] = None, child_tool_runs: List[ToolRun] = None)[source]¶
Bases: BaseRun
Class for ChainRun.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param child_chain_runs: List[langchain.callbacks.tracers.schemas.ChainRun] [Optional]¶
param child_execution_order: int [Required]¶
param child_llm_runs: List[langchain.callbacks.tracers.schemas.LLMRun] [Optional]¶
param child_tool_runs: List[langchain.callbacks.tracers.schemas.ToolRun] [Optional]¶
param end_time: datetime.datetime [Optional]¶
param error: Optional[str] = None¶
param execution_order: int [Required]¶
param extra: Optional[Dict[str, Any]] = None¶
param inputs: Dict[str, Any] [Required]¶
param outputs: Optional[Dict[str, Any]] = None¶
param parent_uuid: Optional[str] = None¶
param serialized: Dict[str, Any] [Required]¶
param session_id: int [Required]¶
param start_time: datetime.datetime [Optional]¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.schemas.ChainRun.html
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e63bed603f73-1
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param start_time: datetime.datetime [Optional]¶
param uuid: str [Required]¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.schemas.ChainRun.html
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c407f45eccd5-0
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langchain.callbacks.mlflow_callback.import_mlflow¶
langchain.callbacks.mlflow_callback.import_mlflow() → Any[source]¶
Import the mlflow python package and raise an error if it is not installed.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.mlflow_callback.import_mlflow.html
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633f78b68953-0
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langchain.callbacks.wandb_callback.analyze_text¶
langchain.callbacks.wandb_callback.analyze_text(text: str, complexity_metrics: bool = True, visualize: bool = True, nlp: Any = None, output_dir: Optional[Union[str, Path]] = None) → dict[source]¶
Analyze text using textstat and spacy.
Parameters
text (str) – The text to analyze.
complexity_metrics (bool) – Whether to compute complexity metrics.
visualize (bool) – Whether to visualize the text.
nlp (spacy.lang) – The spacy language model to use for visualization.
output_dir (str) – The directory to save the visualization files to.
Returns
A dictionary containing the complexity metrics and visualizationfiles serialized in a wandb.Html element.
Return type
(dict)
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.wandb_callback.analyze_text.html
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beb9467d41bc-0
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langchain.callbacks.manager.RunManager¶
class langchain.callbacks.manager.RunManager(*, run_id: UUID, handlers: List[BaseCallbackHandler], inheritable_handlers: List[BaseCallbackHandler], parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, inheritable_tags: Optional[List[str]] = None)[source]¶
Bases: BaseRunManager
Sync Run Manager.
Initialize the run manager.
Parameters
run_id (UUID) – The ID of the run.
handlers (List[BaseCallbackHandler]) – The list of handlers.
inheritable_handlers (List[BaseCallbackHandler]) – The list of inheritable handlers.
parent_run_id (UUID, optional) – The ID of the parent run.
Defaults to None.
tags (Optional[List[str]]) – The list of tags.
inheritable_tags (Optional[List[str]]) – The list of inheritable tags.
Methods
__init__(*, run_id, handlers, ...[, ...])
Initialize the run manager.
get_noop_manager()
Return a manager that doesn't perform any operations.
on_text(text, **kwargs)
Run when text is received.
classmethod get_noop_manager() → BRM¶
Return a manager that doesn’t perform any operations.
Returns
The noop manager.
Return type
BaseRunManager
on_text(text: str, **kwargs: Any) → Any[source]¶
Run when text is received.
Parameters
text (str) – The received text.
Returns
The result of the callback.
Return type
Any
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.RunManager.html
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cac8d98680b7-0
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langchain.callbacks.flyte_callback.FlyteCallbackHandler¶
class langchain.callbacks.flyte_callback.FlyteCallbackHandler[source]¶
Bases: BaseMetadataCallbackHandler, BaseCallbackHandler
This callback handler is designed specifically for usage within a Flyte task.
Initialize callback handler.
Methods
__init__()
Initialize callback handler.
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(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.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.flyte_callback.FlyteCallbackHandler.html
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cac8d98680b7-1
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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.
raise_error
run_inline
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: Union[Exception, KeyboardInterrupt], **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, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
Run when LLM ends running.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.flyte_callback.FlyteCallbackHandler.html
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cac8d98680b7-2
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Run when LLM ends running.
on_llm_error(error: Union[Exception, KeyboardInterrupt], **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: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
on_retriever_start(query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
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: Union[Exception, KeyboardInterrupt], **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.
property always_verbose: bool¶
Whether to call verbose callbacks even if verbose is False.
property ignore_agent: bool¶
Whether to ignore agent callbacks.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.flyte_callback.FlyteCallbackHandler.html
|
cac8d98680b7-3
|
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.
raise_error: bool = False¶
run_inline: bool = False¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.flyte_callback.FlyteCallbackHandler.html
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e71a37f66c04-0
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langchain.callbacks.base.AsyncCallbackHandler¶
class langchain.callbacks.base.AsyncCallbackHandler[source]¶
Bases: BaseCallbackHandler
Async callback handler that can be used to handle callbacks from langchain.
Methods
__init__()
on_agent_action(action, *, run_id[, ...])
Run on agent action.
on_agent_finish(finish, *, run_id[, ...])
Run on agent end.
on_chain_end(outputs, *, run_id[, parent_run_id])
Run when chain ends running.
on_chain_error(error, *, run_id[, parent_run_id])
Run when chain errors.
on_chain_start(serialized, inputs, *, run_id)
Run when chain starts running.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, *, run_id[, parent_run_id])
Run when LLM ends running.
on_llm_error(error, *, run_id[, parent_run_id])
Run when LLM errors.
on_llm_new_token(token, *, run_id[, ...])
Run on new LLM token.
on_llm_start(serialized, prompts, *, run_id)
Run when LLM starts running.
on_retriever_end(documents, *, run_id[, ...])
Run on retriever end.
on_retriever_error(error, *, run_id[, ...])
Run on retriever error.
on_retriever_start(query, *, run_id[, ...])
Run on retriever start.
on_text(text, *, run_id[, parent_run_id])
Run on arbitrary text.
on_tool_end(output, *, run_id[, parent_run_id])
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.base.AsyncCallbackHandler.html
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e71a37f66c04-1
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on_tool_end(output, *, run_id[, parent_run_id])
Run when tool ends running.
on_tool_error(error, *, run_id[, parent_run_id])
Run when tool errors.
on_tool_start(serialized, input_str, *, run_id)
Run when tool starts running.
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.
raise_error
run_inline
async on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None[source]¶
Run on agent action.
async on_agent_finish(finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None[source]¶
Run on agent end.
async on_chain_end(outputs: Dict[str, Any], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None[source]¶
Run when chain ends running.
async on_chain_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None[source]¶
Run when chain errors.
async on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None[source]¶
Run when chain starts running.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.base.AsyncCallbackHandler.html
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e71a37f66c04-2
|
Run when chain starts running.
async 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, **kwargs: Any) → Any[source]¶
Run when a chat model starts running.
async on_llm_end(response: LLMResult, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None[source]¶
Run when LLM ends running.
async on_llm_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None[source]¶
Run when LLM errors.
async on_llm_new_token(token: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None[source]¶
Run on new LLM token. Only available when streaming is enabled.
async on_llm_start(serialized: Dict[str, Any], prompts: List[str], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None[source]¶
Run when LLM starts running.
async on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None[source]¶
Run on retriever end.
async on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None[source]¶
Run on retriever error.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.base.AsyncCallbackHandler.html
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e71a37f66c04-3
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Run on retriever error.
async on_retriever_start(query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None[source]¶
Run on retriever start.
async on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None[source]¶
Run on arbitrary text.
async on_tool_end(output: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None[source]¶
Run when tool ends running.
async on_tool_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None[source]¶
Run when tool errors.
async on_tool_start(serialized: Dict[str, Any], input_str: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None[source]¶
Run when tool starts running.
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.
raise_error: bool = False¶
run_inline: bool = False¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.base.AsyncCallbackHandler.html
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d416426e10c4-0
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langchain.callbacks.tracers.schemas.TracerSession¶
class langchain.callbacks.tracers.schemas.TracerSession(*, start_time: datetime = None, name: Optional[str] = None, extra: Optional[Dict[str, Any]] = None, tenant_id: UUID, id: UUID)[source]¶
Bases: TracerSessionBase
TracerSessionV1 schema for the V2 API.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param extra: Optional[Dict[str, Any]] = None¶
param id: uuid.UUID [Required]¶
param name: Optional[str] = None¶
param start_time: datetime.datetime [Optional]¶
param tenant_id: uuid.UUID [Required]¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.schemas.TracerSession.html
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1f8a7884f578-0
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langchain.callbacks.tracers.schemas.TracerSessionV1¶
class langchain.callbacks.tracers.schemas.TracerSessionV1(*, start_time: datetime = None, name: Optional[str] = None, extra: Optional[Dict[str, Any]] = None, id: int)[source]¶
Bases: TracerSessionV1Base
TracerSessionV1 schema.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param extra: Optional[Dict[str, Any]] = None¶
param id: int [Required]¶
param name: Optional[str] = None¶
param start_time: datetime.datetime [Optional]¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.schemas.TracerSessionV1.html
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53d304696991-0
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langchain.callbacks.arthur_callback.ArthurCallbackHandler¶
class langchain.callbacks.arthur_callback.ArthurCallbackHandler(arthur_model: ArthurModel)[source]¶
Bases: BaseCallbackHandler
Callback Handler that logs to Arthur platform.
Arthur helps enterprise teams optimize model operations
and performance at scale. The Arthur API tracks model
performance, explainability, and fairness across tabular,
NLP, and CV models. Our API is model- and platform-agnostic,
and continuously scales with complex and dynamic enterprise needs.
To learn more about Arthur, visit our website at
https://www.arthur.ai/ or read the Arthur docs at
https://docs.arthur.ai/
Initialize callback handler.
Methods
__init__(arthur_model)
Initialize callback handler.
from_credentials(model_id[, arthur_url, ...])
Initialize callback handler from Arthur credentials.
on_agent_action(action, **kwargs)
Do nothing when agent takes a specific action.
on_agent_finish(finish, **kwargs)
Do nothing
on_chain_end(outputs, **kwargs)
On chain end, do nothing.
on_chain_error(error, **kwargs)
Do nothing when LLM chain outputs an error.
on_chain_start(serialized, inputs, **kwargs)
On chain start, do nothing.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, **kwargs)
On LLM end, send data to Arthur.
on_llm_error(error, **kwargs)
Do nothing when LLM outputs an error.
on_llm_new_token(token, **kwargs)
On new token, pass.
on_llm_start(serialized, prompts, **kwargs)
On LLM start, save the input prompts
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.arthur_callback.ArthurCallbackHandler.html
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53d304696991-1
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On LLM start, save the input prompts
on_retriever_end(documents, *, run_id[, ...])
Run when Retriever ends running.
on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
on_retriever_start(query, *, run_id[, ...])
Run when Retriever starts running.
on_text(text, **kwargs)
Do nothing
on_tool_end(output[, observation_prefix, ...])
Do nothing when tool ends.
on_tool_error(error, **kwargs)
Do nothing when tool outputs an error.
on_tool_start(serialized, input_str, **kwargs)
Do nothing when tool starts.
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.
raise_error
run_inline
classmethod from_credentials(model_id: str, arthur_url: Optional[str] = 'https://app.arthur.ai', arthur_login: Optional[str] = None, arthur_password: Optional[str] = None) → ArthurCallbackHandler[source]¶
Initialize callback handler from Arthur credentials.
Parameters
model_id (str) – The ID of the arthur model to log to.
arthur_url (str, optional) – The URL of the Arthur instance to log to.
Defaults to “https://app.arthur.ai”.
arthur_login (str, optional) – The login to use to connect to Arthur.
Defaults to None.
arthur_password (str, optional) – The password to use to connect to
Arthur. Defaults to None.
Returns
The initialized callback handler.
Return type
ArthurCallbackHandler
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.arthur_callback.ArthurCallbackHandler.html
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53d304696991-2
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Returns
The initialized callback handler.
Return type
ArthurCallbackHandler
on_agent_action(action: AgentAction, **kwargs: Any) → Any[source]¶
Do nothing when agent takes a specific action.
on_agent_finish(finish: AgentFinish, **kwargs: Any) → None[source]¶
Do nothing
on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None[source]¶
On chain end, do nothing.
on_chain_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Do nothing when LLM chain outputs an error.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) → None[source]¶
On chain start, do nothing.
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, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
On LLM end, send data to Arthur.
on_llm_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Do nothing when LLM outputs an error.
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
On new token, pass.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
On LLM start, save the input prompts
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.arthur_callback.ArthurCallbackHandler.html
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53d304696991-3
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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(query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
on_text(text: str, **kwargs: Any) → None[source]¶
Do nothing
on_tool_end(output: str, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any) → None[source]¶
Do nothing when tool ends.
on_tool_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Do nothing when tool outputs an error.
on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None[source]¶
Do nothing when tool starts.
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.
raise_error: bool = False¶
run_inline: bool = False¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.arthur_callback.ArthurCallbackHandler.html
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72722bb22ae8-0
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langchain.callbacks.utils.import_textstat¶
langchain.callbacks.utils.import_textstat() → Any[source]¶
Import the textstat python package and raise an error if it is not installed.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.utils.import_textstat.html
|
e7691f5faecf-0
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langchain.callbacks.argilla_callback.ArgillaCallbackHandler¶
class langchain.callbacks.argilla_callback.ArgillaCallbackHandler(dataset_name: str, workspace_name: Optional[str] = None, api_url: Optional[str] = None, api_key: Optional[str] = None)[source]¶
Bases: BaseCallbackHandler
Callback Handler that logs into Argilla.
Parameters
dataset_name – name of the FeedbackDataset in Argilla. Note that it must
exist in advance. If you need help on how to create a FeedbackDataset in
Argilla, please visit
https://docs.argilla.io/en/latest/guides/llms/practical_guides/use_argilla_callback_in_langchain.html.
workspace_name – name of the workspace in Argilla where the specified
FeedbackDataset lives in. Defaults to None, which means that the
default workspace will be used.
api_url – URL of the Argilla Server that we want to use, and where the
FeedbackDataset lives in. Defaults to None, which means that either
ARGILLA_API_URL environment variable or the default http://localhost:6900
will be used.
api_key – API Key to connect to the Argilla Server. Defaults to None, which
means that either ARGILLA_API_KEY environment variable or the default
argilla.apikey will be used.
Raises
ImportError – if the argilla package is not installed.
ConnectionError – if the connection to Argilla fails.
FileNotFoundError – if the FeedbackDataset retrieval from Argilla fails.
Examples
>>> from langchain.llms import OpenAI
>>> from langchain.callbacks import ArgillaCallbackHandler
>>> argilla_callback = ArgillaCallbackHandler(
... dataset_name="my-dataset",
... workspace_name="my-workspace",
... api_url="http://localhost:6900",
... api_key="argilla.apikey",
... )
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.argilla_callback.ArgillaCallbackHandler.html
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e7691f5faecf-1
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... api_key="argilla.apikey",
... )
>>> llm = OpenAI(
... temperature=0,
... callbacks=[argilla_callback],
... verbose=True,
... openai_api_key="API_KEY_HERE",
... )
>>> llm.generate([
... "What is the best NLP-annotation tool out there? (no bias at all)",
... ])
"Argilla, no doubt about it."
Initializes the ArgillaCallbackHandler.
Parameters
dataset_name – name of the FeedbackDataset in Argilla. Note that it must
exist in advance. If you need help on how to create a FeedbackDataset
in Argilla, please visit
https://docs.argilla.io/en/latest/guides/llms/practical_guides/use_argilla_callback_in_langchain.html.
workspace_name – name of the workspace in Argilla where the specified
FeedbackDataset lives in. Defaults to None, which means that the
default workspace will be used.
api_url – URL of the Argilla Server that we want to use, and where the
FeedbackDataset lives in. Defaults to None, which means that either
ARGILLA_API_URL environment variable or the default
http://localhost:6900 will be used.
api_key – API Key to connect to the Argilla Server. Defaults to None, which
means that either ARGILLA_API_KEY environment variable or the default
argilla.apikey will be used.
Raises
ImportError – if the argilla package is not installed.
ConnectionError – if the connection to Argilla fails.
FileNotFoundError – if the FeedbackDataset retrieval from Argilla fails.
Methods
__init__(dataset_name[, workspace_name, ...])
Initializes the ArgillaCallbackHandler.
on_agent_action(action, **kwargs)
Do nothing when agent takes a specific action.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.argilla_callback.ArgillaCallbackHandler.html
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e7691f5faecf-2
|
on_agent_action(action, **kwargs)
Do nothing when agent takes a specific action.
on_agent_finish(finish, **kwargs)
Do nothing
on_chain_end(outputs, **kwargs)
If either the parent_run_id or the run_id is in self.prompts, then log the outputs to Argilla, and pop the run from self.prompts.
on_chain_error(error, **kwargs)
Do nothing when LLM chain outputs an error.
on_chain_start(serialized, inputs, **kwargs)
If the key input is in inputs, then save it in self.prompts using either the parent_run_id or the run_id as the key.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, **kwargs)
Log records to Argilla when an LLM ends.
on_llm_error(error, **kwargs)
Do nothing when LLM outputs an error.
on_llm_new_token(token, **kwargs)
Do nothing when a new token is generated.
on_llm_start(serialized, prompts, **kwargs)
Save the prompts in memory when an 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(query, *, run_id[, ...])
Run when Retriever starts running.
on_text(text, **kwargs)
Do nothing
on_tool_end(output[, observation_prefix, ...])
Do nothing when tool ends.
on_tool_error(error, **kwargs)
Do nothing when tool outputs an error.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.argilla_callback.ArgillaCallbackHandler.html
|
e7691f5faecf-3
|
on_tool_error(error, **kwargs)
Do nothing when tool outputs an error.
on_tool_start(serialized, input_str, **kwargs)
Do nothing when tool starts.
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.
raise_error
run_inline
on_agent_action(action: AgentAction, **kwargs: Any) → Any[source]¶
Do nothing when agent takes a specific action.
on_agent_finish(finish: AgentFinish, **kwargs: Any) → None[source]¶
Do nothing
on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None[source]¶
If either the parent_run_id or the run_id is in self.prompts, then
log the outputs to Argilla, and pop the run from self.prompts. The behavior
differs if the output is a list or not.
on_chain_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Do nothing when LLM chain outputs an error.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) → None[source]¶
If the key input is in inputs, then save it in self.prompts using
either the parent_run_id or the run_id as the key. This is done so that
we don’t log the same input prompt twice, once when the LLM starts and once
when the chain starts.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.argilla_callback.ArgillaCallbackHandler.html
|
e7691f5faecf-4
|
when the chain starts.
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, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
Log records to Argilla when an LLM ends.
on_llm_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Do nothing when LLM outputs an error.
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Do nothing when a new token is generated.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
Save the prompts in memory when an 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(query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
on_text(text: str, **kwargs: Any) → None[source]¶
Do nothing
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.argilla_callback.ArgillaCallbackHandler.html
|
e7691f5faecf-5
|
on_text(text: str, **kwargs: Any) → None[source]¶
Do nothing
on_tool_end(output: str, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any) → None[source]¶
Do nothing when tool ends.
on_tool_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Do nothing when tool outputs an error.
on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None[source]¶
Do nothing when tool starts.
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.
raise_error: bool = False¶
run_inline: bool = False¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.argilla_callback.ArgillaCallbackHandler.html
|
59429a8a76e9-0
|
langchain.callbacks.manager.tracing_enabled¶
langchain.callbacks.manager.tracing_enabled(session_name: str = 'default') → Generator[TracerSessionV1, None, None][source]¶
Get the Deprecated LangChainTracer in a context manager.
Parameters
session_name (str, optional) – The name of the session.
Defaults to “default”.
Returns
The LangChainTracer session.
Return type
TracerSessionV1
Example
>>> with tracing_enabled() as session:
... # Use the LangChainTracer session
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.tracing_enabled.html
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f79bb50f00d2-0
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langchain.callbacks.manager.CallbackManagerForToolRun¶
class langchain.callbacks.manager.CallbackManagerForToolRun(*, run_id: UUID, handlers: List[BaseCallbackHandler], inheritable_handlers: List[BaseCallbackHandler], parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, inheritable_tags: Optional[List[str]] = None)[source]¶
Bases: RunManager, ToolManagerMixin
Callback manager for tool run.
Initialize the run manager.
Parameters
run_id (UUID) – The ID of the run.
handlers (List[BaseCallbackHandler]) – The list of handlers.
inheritable_handlers (List[BaseCallbackHandler]) – The list of inheritable handlers.
parent_run_id (UUID, optional) – The ID of the parent run.
Defaults to None.
tags (Optional[List[str]]) – The list of tags.
inheritable_tags (Optional[List[str]]) – The list of inheritable tags.
Methods
__init__(*, run_id, handlers, ...[, ...])
Initialize the run manager.
get_child([tag])
Get a child callback manager.
get_noop_manager()
Return a manager that doesn't perform any operations.
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.
get_child(tag: Optional[str] = None) → CallbackManager[source]¶
Get a child callback manager.
Parameters
tag (str, optional) – The tag for the child callback manager.
Defaults to None.
Returns
The child callback manager.
Return type
CallbackManager
classmethod get_noop_manager() → BRM¶
Return a manager that doesn’t perform any operations.
Returns
The noop manager.
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.CallbackManagerForToolRun.html
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f79bb50f00d2-1
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Return a manager that doesn’t perform any operations.
Returns
The noop manager.
Return type
BaseRunManager
on_text(text: str, **kwargs: Any) → Any¶
Run when text is received.
Parameters
text (str) – The received text.
Returns
The result of the callback.
Return type
Any
on_tool_end(output: str, **kwargs: Any) → None[source]¶
Run when tool ends running.
Parameters
output (str) – The output of the tool.
on_tool_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when tool errors.
Parameters
error (Exception or KeyboardInterrupt) – The error.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.CallbackManagerForToolRun.html
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e433e35f4bac-0
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langchain.callbacks.tracers.schemas.TracerSessionV1Base¶
class langchain.callbacks.tracers.schemas.TracerSessionV1Base(*, start_time: datetime = None, name: Optional[str] = None, extra: Optional[Dict[str, Any]] = None)[source]¶
Bases: BaseModel
Base class for TracerSessionV1.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param extra: Optional[Dict[str, Any]] = None¶
param name: Optional[str] = None¶
param start_time: datetime.datetime [Optional]¶
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.schemas.TracerSessionV1Base.html
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ac68aa3d54c9-0
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langchain.callbacks.manager.wandb_tracing_enabled¶
langchain.callbacks.manager.wandb_tracing_enabled(session_name: str = 'default') → Generator[None, None, None][source]¶
Get the WandbTracer in a context manager.
Parameters
session_name (str, optional) – The name of the session.
Defaults to “default”.
Returns
None
Example
>>> with wandb_tracing_enabled() as session:
... # Use the WandbTracer session
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.wandb_tracing_enabled.html
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3033792c62da-0
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langchain.callbacks.flyte_callback.analyze_text¶
langchain.callbacks.flyte_callback.analyze_text(text: str, nlp: Any = None) → dict[source]¶
Analyze text using textstat and spacy.
Parameters
text (str) – The text to analyze.
nlp (spacy.lang) – The spacy language model to use for visualization.
Returns
A dictionary containing the complexity metrics and visualizationfiles serialized to HTML string.
Return type
(dict)
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.flyte_callback.analyze_text.html
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326a8b690642-0
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langchain.callbacks.manager.AsyncRunManager¶
class langchain.callbacks.manager.AsyncRunManager(*, run_id: UUID, handlers: List[BaseCallbackHandler], inheritable_handlers: List[BaseCallbackHandler], parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, inheritable_tags: Optional[List[str]] = None)[source]¶
Bases: BaseRunManager
Async Run Manager.
Initialize the run manager.
Parameters
run_id (UUID) – The ID of the run.
handlers (List[BaseCallbackHandler]) – The list of handlers.
inheritable_handlers (List[BaseCallbackHandler]) – The list of inheritable handlers.
parent_run_id (UUID, optional) – The ID of the parent run.
Defaults to None.
tags (Optional[List[str]]) – The list of tags.
inheritable_tags (Optional[List[str]]) – The list of inheritable tags.
Methods
__init__(*, run_id, handlers, ...[, ...])
Initialize the run manager.
get_noop_manager()
Return a manager that doesn't perform any operations.
on_text(text, **kwargs)
Run when text is received.
classmethod get_noop_manager() → BRM¶
Return a manager that doesn’t perform any operations.
Returns
The noop manager.
Return type
BaseRunManager
async on_text(text: str, **kwargs: Any) → Any[source]¶
Run when text is received.
Parameters
text (str) – The received text.
Returns
The result of the callback.
Return type
Any
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.AsyncRunManager.html
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dd45a26c6fe7-0
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langchain.callbacks.streaming_stdout_final_only.FinalStreamingStdOutCallbackHandler¶
class langchain.callbacks.streaming_stdout_final_only.FinalStreamingStdOutCallbackHandler(*, answer_prefix_tokens: Optional[List[str]] = None, strip_tokens: bool = True, stream_prefix: bool = False)[source]¶
Bases: StreamingStdOutCallbackHandler
Callback handler for streaming in agents.
Only works with agents using LLMs that support streaming.
Only the final output of the agent will be streamed.
Instantiate FinalStreamingStdOutCallbackHandler.
Parameters
answer_prefix_tokens – Token sequence that prefixes the anwer.
Default is [“Final”, “Answer”, “:”]
strip_tokens – Ignore white spaces and new lines when comparing
answer_prefix_tokens to last tokens? (to determine if answer has been
reached)
stream_prefix – Should answer prefix itself also be streamed?
Methods
__init__(*[, answer_prefix_tokens, ...])
Instantiate FinalStreamingStdOutCallbackHandler.
append_to_last_tokens(token)
check_if_answer_reached()
on_agent_action(action, **kwargs)
Run on agent action.
on_agent_finish(finish, **kwargs)
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.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streaming_stdout_final_only.FinalStreamingStdOutCallbackHandler.html
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dd45a26c6fe7-1
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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(query, *, run_id[, ...])
Run when Retriever starts running.
on_text(text, **kwargs)
Run on arbitrary text.
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.
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.
raise_error
run_inline
append_to_last_tokens(token: str) → None[source]¶
check_if_answer_reached() → bool[source]¶
on_agent_action(action: AgentAction, **kwargs: Any) → Any¶
Run on agent action.
on_agent_finish(finish: AgentFinish, **kwargs: Any) → None¶
Run on agent end.
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.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streaming_stdout_final_only.FinalStreamingStdOutCallbackHandler.html
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dd45a26c6fe7-2
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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, **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[source]¶
Run on new LLM token. Only available when streaming is enabled.
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: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
on_retriever_start(query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
on_text(text: str, **kwargs: Any) → None¶
Run on arbitrary text.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streaming_stdout_final_only.FinalStreamingStdOutCallbackHandler.html
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dd45a26c6fe7-3
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Run on arbitrary text.
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.
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.
raise_error: bool = False¶
run_inline: bool = False¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streaming_stdout_final_only.FinalStreamingStdOutCallbackHandler.html
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56b35f8fc854-0
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langchain.callbacks.tracers.wandb.WandbRunArgs¶
class langchain.callbacks.tracers.wandb.WandbRunArgs[source]¶
Bases: TypedDict
Arguments for the WandbTracer.
Methods
__init__(*args, **kwargs)
clear()
copy()
fromkeys([value])
Create a new dictionary with keys from iterable and values set to value.
get(key[, default])
Return the value for key if key is in the dictionary, else default.
items()
keys()
pop(k[,d])
If the key is not found, return the default if given; otherwise, raise a KeyError.
popitem()
Remove and return a (key, value) pair as a 2-tuple.
setdefault(key[, default])
Insert key with a value of default if key is not in the dictionary.
update([E, ]**F)
If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values()
Attributes
job_type
dir
config
project
entity
reinit
tags
group
name
notes
magic
config_exclude_keys
config_include_keys
anonymous
mode
allow_val_change
resume
force
tensorboard
sync_tensorboard
monitor_gym
save_code
id
settings
clear() → None. Remove all items from D.¶
copy() → a shallow copy of D¶
fromkeys(value=None, /)¶
Create a new dictionary with keys from iterable and values set to value.
get(key, default=None, /)¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.wandb.WandbRunArgs.html
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56b35f8fc854-1
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get(key, default=None, /)¶
Return the value for key if key is in the dictionary, else default.
items() → a set-like object providing a view on D's items¶
keys() → a set-like object providing a view on D's keys¶
pop(k[, d]) → v, remove specified key and return the corresponding value.¶
If the key is not found, return the default if given; otherwise,
raise a KeyError.
popitem()¶
Remove and return a (key, value) pair as a 2-tuple.
Pairs are returned in LIFO (last-in, first-out) order.
Raises KeyError if the dict is empty.
setdefault(key, default=None, /)¶
Insert key with a value of default if key is not in the dictionary.
Return the value for key if key is in the dictionary, else default.
update([E, ]**F) → None. Update D from dict/iterable E and F.¶
If E is present and has a .keys() method, then does: for k in E: D[k] = E[k]
If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v
In either case, this is followed by: for k in F: D[k] = F[k]
values() → an object providing a view on D's values¶
allow_val_change: Optional[bool]¶
anonymous: Optional[str]¶
config: Union[Dict, str, None]¶
config_exclude_keys: Optional[List[str]]¶
config_include_keys: Optional[List[str]]¶
dir: Optional[StrPath]¶
entity: Optional[str]¶
force: Optional[bool]¶
group: Optional[str]¶
id: Optional[str]¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.wandb.WandbRunArgs.html
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56b35f8fc854-2
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group: Optional[str]¶
id: Optional[str]¶
job_type: Optional[str]¶
magic: Optional[Union[dict, str, bool]]¶
mode: Optional[str]¶
monitor_gym: Optional[bool]¶
name: Optional[str]¶
notes: Optional[str]¶
project: Optional[str]¶
reinit: Optional[bool]¶
resume: Optional[Union[bool, str]]¶
save_code: Optional[bool]¶
settings: Union[WBSettings, Dict[str, Any], None]¶
sync_tensorboard: Optional[bool]¶
tags: Optional[Sequence]¶
tensorboard: Optional[bool]¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.wandb.WandbRunArgs.html
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7ea4b31db217-0
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langchain.callbacks.manager.CallbackManagerForLLMRun¶
class langchain.callbacks.manager.CallbackManagerForLLMRun(*, run_id: UUID, handlers: List[BaseCallbackHandler], inheritable_handlers: List[BaseCallbackHandler], parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, inheritable_tags: Optional[List[str]] = None)[source]¶
Bases: RunManager, LLMManagerMixin
Callback manager for LLM run.
Initialize the run manager.
Parameters
run_id (UUID) – The ID of the run.
handlers (List[BaseCallbackHandler]) – The list of handlers.
inheritable_handlers (List[BaseCallbackHandler]) – The list of inheritable handlers.
parent_run_id (UUID, optional) – The ID of the parent run.
Defaults to None.
tags (Optional[List[str]]) – The list of tags.
inheritable_tags (Optional[List[str]]) – The list of inheritable tags.
Methods
__init__(*, run_id, handlers, ...[, ...])
Initialize the run manager.
get_noop_manager()
Return a manager that doesn't perform any operations.
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_text(text, **kwargs)
Run when text is received.
classmethod get_noop_manager() → BRM¶
Return a manager that doesn’t perform any operations.
Returns
The noop manager.
Return type
BaseRunManager
on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
Run when LLM ends running.
Parameters
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.CallbackManagerForLLMRun.html
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7ea4b31db217-1
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Run when LLM ends running.
Parameters
response (LLMResult) – The LLM result.
on_llm_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when LLM errors.
Parameters
error (Exception or KeyboardInterrupt) – The error.
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Run when LLM generates a new token.
Parameters
token (str) – The new token.
on_text(text: str, **kwargs: Any) → Any¶
Run when text is received.
Parameters
text (str) – The received text.
Returns
The result of the callback.
Return type
Any
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.CallbackManagerForLLMRun.html
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4051567e80e7-0
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langchain.callbacks.streamlit.mutable_expander.ChildType¶
class langchain.callbacks.streamlit.mutable_expander.ChildType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Bases: Enum
Attributes
MARKDOWN
EXCEPTION
EXCEPTION = 'EXCEPTION'¶
MARKDOWN = 'MARKDOWN'¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streamlit.mutable_expander.ChildType.html
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1b62933a147f-0
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langchain.callbacks.manager.BaseRunManager¶
class langchain.callbacks.manager.BaseRunManager(*, run_id: UUID, handlers: List[BaseCallbackHandler], inheritable_handlers: List[BaseCallbackHandler], parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, inheritable_tags: Optional[List[str]] = None)[source]¶
Bases: RunManagerMixin
Base class for run manager (a bound callback manager).
Initialize the run manager.
Parameters
run_id (UUID) – The ID of the run.
handlers (List[BaseCallbackHandler]) – The list of handlers.
inheritable_handlers (List[BaseCallbackHandler]) – The list of inheritable handlers.
parent_run_id (UUID, optional) – The ID of the parent run.
Defaults to None.
tags (Optional[List[str]]) – The list of tags.
inheritable_tags (Optional[List[str]]) – The list of inheritable tags.
Methods
__init__(*, run_id, handlers, ...[, ...])
Initialize the run manager.
get_noop_manager()
Return a manager that doesn't perform any operations.
on_text(text, *, run_id[, parent_run_id])
Run on arbitrary text.
classmethod get_noop_manager() → BRM[source]¶
Return a manager that doesn’t perform any operations.
Returns
The noop manager.
Return type
BaseRunManager
on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on arbitrary text.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.BaseRunManager.html
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487155036464-0
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langchain.callbacks.human.HumanRejectedException¶
class langchain.callbacks.human.HumanRejectedException[source]¶
Bases: Exception
Exception to raise when a person manually review and rejects a value.
add_note()¶
Exception.add_note(note) –
add a note to the exception
with_traceback()¶
Exception.with_traceback(tb) –
set self.__traceback__ to tb and return self.
args¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.human.HumanRejectedException.html
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74cc3846af0e-0
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langchain.callbacks.manager.AsyncCallbackManagerForRetrieverRun¶
class langchain.callbacks.manager.AsyncCallbackManagerForRetrieverRun(*, run_id: UUID, handlers: List[BaseCallbackHandler], inheritable_handlers: List[BaseCallbackHandler], parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, inheritable_tags: Optional[List[str]] = None)[source]¶
Bases: AsyncRunManager, RetrieverManagerMixin
Async callback manager for retriever run.
Initialize the run manager.
Parameters
run_id (UUID) – The ID of the run.
handlers (List[BaseCallbackHandler]) – The list of handlers.
inheritable_handlers (List[BaseCallbackHandler]) – The list of inheritable handlers.
parent_run_id (UUID, optional) – The ID of the parent run.
Defaults to None.
tags (Optional[List[str]]) – The list of tags.
inheritable_tags (Optional[List[str]]) – The list of inheritable tags.
Methods
__init__(*, run_id, handlers, ...[, ...])
Initialize the run manager.
get_child([tag])
Get a child callback manager.
get_noop_manager()
Return a manager that doesn't perform any operations.
on_retriever_end(documents, **kwargs)
Run when retriever ends running.
on_retriever_error(error, **kwargs)
Run when retriever errors.
on_text(text, **kwargs)
Run when text is received.
get_child(tag: Optional[str] = None) → AsyncCallbackManager[source]¶
Get a child callback manager.
classmethod get_noop_manager() → BRM¶
Return a manager that doesn’t perform any operations.
Returns
The noop manager.
Return type
BaseRunManager
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.AsyncCallbackManagerForRetrieverRun.html
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74cc3846af0e-1
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Returns
The noop manager.
Return type
BaseRunManager
async on_retriever_end(documents: Sequence[Document], **kwargs: Any) → None[source]¶
Run when retriever ends running.
async on_retriever_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when retriever errors.
async on_text(text: str, **kwargs: Any) → Any¶
Run when text is received.
Parameters
text (str) – The received text.
Returns
The result of the callback.
Return type
Any
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.AsyncCallbackManagerForRetrieverRun.html
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6d61979c76cd-0
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langchain.callbacks.openai_info.OpenAICallbackHandler¶
class langchain.callbacks.openai_info.OpenAICallbackHandler[source]¶
Bases: BaseCallbackHandler
Callback Handler that tracks OpenAI info.
Methods
__init__()
on_agent_action(action, *, run_id[, ...])
Run on agent action.
on_agent_finish(finish, *, run_id[, ...])
Run on agent end.
on_chain_end(outputs, *, run_id[, parent_run_id])
Run when chain ends running.
on_chain_error(error, *, run_id[, parent_run_id])
Run when chain errors.
on_chain_start(serialized, inputs, *, run_id)
Run when chain starts running.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, **kwargs)
Collect token usage.
on_llm_error(error, *, run_id[, parent_run_id])
Run when LLM errors.
on_llm_new_token(token, **kwargs)
Print out the token.
on_llm_start(serialized, prompts, **kwargs)
Print out the prompts.
on_retriever_end(documents, *, run_id[, ...])
Run when Retriever ends running.
on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
on_retriever_start(query, *, run_id[, ...])
Run when Retriever starts running.
on_text(text, *, run_id[, parent_run_id])
Run on arbitrary text.
on_tool_end(output, *, run_id[, parent_run_id])
Run when tool ends running.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.openai_info.OpenAICallbackHandler.html
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6d61979c76cd-1
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Run when tool ends running.
on_tool_error(error, *, run_id[, parent_run_id])
Run when tool errors.
on_tool_start(serialized, input_str, *, run_id)
Run when tool starts running.
Attributes
always_verbose
Whether to call verbose callbacks even if verbose is False.
completion_tokens
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.
prompt_tokens
raise_error
run_inline
successful_requests
total_cost
total_tokens
on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent action.
on_agent_finish(finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent end.
on_chain_end(outputs: Dict[str, Any], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when chain ends running.
on_chain_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when chain errors.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → Any¶
Run when chain starts running.
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.openai_info.OpenAICallbackHandler.html
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