viruPVS/langchain-docs huggingface dataset:
langchain_community.callbacks.utils.BaseMetadataCallbackHandler¶
class langchain_community.callbacks.utils.BaseMetadataCallbackHandler[source]¶
This class handles the metadata and associated function states for callbacks.
step¶
The current step.
Type
int
starts¶
The number of times the start method has been called.
Type
int
ends¶
The number of times the end method has been called.
Type
int
errors¶
The number of times the error method has been called.
Type
int
text_ctr¶
The number of times the text method has been called.
Type
int
ignore_llm_¶
Whether to ignore llm callbacks.
Type
bool
ignore_chain_¶
Whether to ignore chain callbacks.
Type
bool
ignore_agent_¶
Whether to ignore agent callbacks.
Type
bool
ignore_retriever_¶
Whether to ignore retriever callbacks.
Type
bool
always_verbose_¶
Whether to always be verbose.
Type
bool
chain_starts¶
The number of times the chain start method has been called.
Type
int
chain_ends¶
The number of times the chain end method has been called.
Type
int
llm_starts¶
The number of times the llm start method has been called.
Type
int
llm_ends¶
The number of times the llm end method has been called.
Type
int
llm_streams¶
The number of times the text method has been called.
Type
int
tool_starts¶
The number of times the tool start method has been called.
Type
int
tool_ends¶
The number of times the tool end method has been called.
Type
int
agent_ends¶
The number of times the agent end method has been called.
Type
int
on_llm_start_records¶
Type
int
on_llm_start_records¶
A list of records of the on_llm_start method.
Type
list
on_llm_token_records¶
A list of records of the on_llm_token method.
Type
list
on_llm_end_records¶
A list of records of the on_llm_end method.
Type
list
on_chain_start_records¶
A list of records of the on_chain_start method.
Type
list
on_chain_end_records¶
A list of records of the on_chain_end method.
Type
list
on_tool_start_records¶
A list of records of the on_tool_start method.
Type
list
on_tool_end_records¶
A list of records of the on_tool_end method.
Type
list
on_agent_finish_records¶
A list of records of the on_agent_end method.
Type
list
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_llm
Whether to ignore LLM callbacks.
Methods
__init__()
get_custom_callback_meta()
reset_callback_meta()
Reset the callback metadata.
__init__() → None[source]¶
get_custom_callback_meta() → Dict[str, Any][source]¶
reset_callback_meta() → None[source]¶
Reset the callback metadata.
langchain_core.callbacks.base.ToolManagerMixin¶
class langchain_core.callbacks.base.ToolManagerMixin[source]¶
Mixin for tool callbacks.
Methods
__init__()
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.
__init__()¶
on_tool_end(output: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any[source]¶
Run when tool ends running.
on_tool_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any[source]¶
Run when tool errors.
langchain_core.callbacks.manager.trace_as_chain_group¶
langchain_core.callbacks.manager.trace_as_chain_group(group_name: str, callback_manager: Optional[CallbackManager] = None, *, inputs: Optional[Dict[str, Any]] = None, project_name: Optional[str] = None, example_id: Optional[Union[str, UUID]] = None, run_id: Optional[UUID] = None, tags: Optional[List[str]] = None) → Generator[CallbackManagerForChainGroup, 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.
callback_manager (CallbackManager, optional) – The callback manager to use.
inputs (Dict[str, Any], optional) – The inputs to 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.
run_id (UUID, optional) – The ID of the run.
tags (List[str], optional) – The inheritable tags to apply to all runs.
Defaults to None.
Note: must have LANGCHAIN_TRACING_V2 env var set to true to see the trace in LangSmith.
Returns
The callback manager for the chain group.
Return type
CallbackManagerForChainGroup
Example
llm_input = "Foo"
with trace_as_chain_group("group_name", inputs={"input": llm_input}) as manager:
    # Use the callback manager for the chain group
    res = llm.predict(llm_input, callbacks=manager)
    manager.on_chain_end({"output": res})
langchain_core.callbacks.manager.BaseRunManager¶
class langchain_core.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, metadata: Optional[Dict[str, Any]] = None, inheritable_metadata: Optional[Dict[str, Any]] = None)[source]¶
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.
metadata (Optional[Dict[str, Any]]) – The metadata.
inheritable_metadata (Optional[Dict[str, Any]]) – The inheritable metadata.
Methods
__init__(*, run_id, handlers, ...[, ...])
Initialize the run manager.
get_noop_manager()
Return a manager that doesn't perform any operations.
on_retry(retry_state, *, run_id[, parent_run_id])
Run on a retry event.
on_text(text, *, run_id[, parent_run_id])
Run on arbitrary text.
Run on arbitrary text.
__init__(*, 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, metadata: Optional[Dict[str, Any]] = None, inheritable_metadata: Optional[Dict[str, Any]] = None) → None[source]¶
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.
metadata (Optional[Dict[str, Any]]) – The metadata.
inheritable_metadata (Optional[Dict[str, Any]]) – The inheritable metadata.
classmethod get_noop_manager() → BRM[source]¶
Return a manager that doesn’t perform any operations.
Returns
The noop manager.
Return type
BaseRunManager
on_retry(retry_state: RetryCallState, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on a retry event.
on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on arbitrary text.
langchain_community.callbacks.mlflow_callback.MlflowCallbackHandler¶
class langchain_community.callbacks.mlflow_callback.MlflowCallbackHandler(name: Optional[str] = 'langchainrun-%', experiment: Optional[str] = 'langchain', tags: Optional[Dict] = None, tracking_uri: Optional[str] = None)[source]¶
Callback Handler that logs metrics and artifacts to mlflow server.
Parameters
name (str) – Name of the run.
experiment (str) – Name of the experiment.
tags (dict) – Tags to be attached for the run.
tracking_uri (str) – MLflow tracking server uri.
This handler will utilize the associated callback method called and formats
the input of each callback function with metadata regarding the state of LLM run,
and adds the response to the list of records for both the {method}_records and
action. It then logs the response to mlflow server.
Initialize callback handler.
Attributes
always_verbose
Whether to call verbose callbacks even if verbose is False.
ignore_agent
Whether to ignore agent callbacks.
ignore_chain
Whether to ignore chain callbacks.
ignore_chat_model
Whether to ignore chat model callbacks.
ignore_llm
Whether to ignore LLM callbacks.
ignore_retriever
Whether to ignore retriever callbacks.
ignore_retry
Whether to ignore retry callbacks.
raise_error
run_inline
Methods
__init__([name, experiment, tags, tracking_uri])
Initialize callback handler.
flush_tracker([langchain_asset, finish])
get_custom_callback_meta()
on_agent_action(action, **kwargs)
Run on agent action.
on_agent_finish(finish, **kwargs)
Run when agent ends running.
on_chain_end(outputs, **kwargs)
Run when chain ends running.
on_chain_error(error, **kwargs)
Run when chain errors.
on_chain_error(error, **kwargs)
Run when chain errors.
on_chain_start(serialized, inputs, **kwargs)
Run when chain starts running.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, **kwargs)
Run when LLM ends running.
on_llm_error(error, **kwargs)
Run when LLM errors.
on_llm_new_token(token, **kwargs)
Run when LLM generates a new token.
on_llm_start(serialized, prompts, **kwargs)
Run when LLM starts.
on_retriever_end(documents, *, run_id[, ...])
Run when Retriever ends running.
on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_retry(retry_state, *, run_id[, parent_run_id])
Run on a retry event.
on_text(text, **kwargs)
Run when agent is ending.
on_tool_end(output, **kwargs)
Run when tool ends running.
on_tool_error(error, **kwargs)
Run when tool errors.
on_tool_start(serialized, input_str, **kwargs)
Run when tool starts running.
reset_callback_meta()
Reset the callback metadata.
__init__(name: Optional[str] = 'langchainrun-%', experiment: Optional[str] = 'langchain', tags: Optional[Dict] = None, tracking_uri: Optional[str] = None) → None[source]¶
Initialize callback handler.
Initialize callback handler.
flush_tracker(langchain_asset: Any = None, finish: bool = False) → None[source]¶
get_custom_callback_meta() → Dict[str, Any]¶
on_agent_action(action: AgentAction, **kwargs: Any) → Any[source]¶
Run on agent action.
on_agent_finish(finish: AgentFinish, **kwargs: Any) → None[source]¶
Run when agent ends running.
on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Run when chain ends running.
on_chain_error(error: BaseException, **kwargs: Any) → None[source]¶
Run when chain errors.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Run when chain starts running.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
Run when LLM ends running.
on_llm_error(error: BaseException, **kwargs: Any) → None[source]¶
Run when LLM errors.
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Run when LLM generates a new token.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
Run when LLM starts.
Run when LLM starts.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever ends running.
on_retriever_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
on_retry(retry_state: RetryCallState, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on a retry event.
on_text(text: str, **kwargs: Any) → None[source]¶
Run when agent is ending.
on_tool_end(output: str, **kwargs: Any) → None[source]¶
Run when tool ends running.
on_tool_error(error: BaseException, **kwargs: Any) → None[source]¶
Run when tool errors.
on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None[source]¶
Run when tool starts running.
reset_callback_meta() → None¶
Reset the callback metadata.
Examples using MlflowCallbackHandler¶
MLflow
langchain_community.callbacks.mlflow_callback.MlflowLogger¶
class langchain_community.callbacks.mlflow_callback.MlflowLogger(**kwargs: Any)[source]¶
Callback Handler that logs metrics and artifacts to mlflow server.
Parameters
name (str) – Name of the run.
experiment (str) – Name of the experiment.
tags (dict) – Tags to be attached for the run.
tracking_uri (str) – MLflow tracking server uri.
This handler implements the helper functions to initialize,
log metrics and artifacts to the mlflow server.
Methods
__init__(**kwargs)
artifact(path)
To upload the file from given path as artifact.
finish_run()
To finish the run.
html(html, filename)
To log the input html string as html file artifact.
jsonf(data, filename)
To log the input data as json file artifact.
langchain_artifact(chain)
metric(key, value)
To log metric to mlflow server.
metrics(data[, step])
To log all metrics in the input dict.
start_run(name, tags)
To start a new run, auto generates the random suffix for name
table(name, dataframe)
To log the input pandas dataframe as a html table
text(text, filename)
To log the input text as text file artifact.
__init__(**kwargs: Any)[source]¶
artifact(path: str) → None[source]¶
To upload the file from given path as artifact.
finish_run() → None[source]¶
To finish the run.
html(html: str, filename: str) → None[source]¶
To log the input html string as html file artifact.
jsonf(data: Dict[str, Any], filename: str) → None[source]¶
To log the input data as json file artifact.
To log the input data as json file artifact.
langchain_artifact(chain: Any) → None[source]¶
metric(key: str, value: float) → None[source]¶
To log metric to mlflow server.
metrics(data: Union[Dict[str, float], Dict[str, int]], step: Optional[int] = 0) → None[source]¶
To log all metrics in the input dict.
start_run(name: str, tags: Dict[str, str]) → None[source]¶
To start a new run, auto generates the random suffix for name
table(name: str, dataframe) → None[source]¶
To log the input pandas dataframe as a html table
text(text: str, filename: str) → None[source]¶
To log the input text as text file artifact.
langchain_community.callbacks.openai_info.OpenAICallbackHandler¶
class langchain_community.callbacks.openai_info.OpenAICallbackHandler[source]¶
Callback Handler that tracks OpenAI info.
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.
ignore_retry
Whether to ignore retry callbacks.
prompt_tokens
raise_error
run_inline
successful_requests
total_cost
total_tokens
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. :param error: The error that occurred. :type error: BaseException :param kwargs: Additional keyword arguments. - response (LLMResult): The response which was generated before                    the error occurred. :type kwargs: Any.
on_llm_new_token(token, **kwargs)
Print out the token.
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(serialized, query, *, run_id)
Run when Retriever starts running.
on_retry(retry_state, *, run_id[, parent_run_id])
Run on a retry event.
on_text(text, *, 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.
__init__()¶
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: BaseException, *, 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, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when chain starts running.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
Collect token usage.
on_llm_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when LLM errors.
:param error: The error that occurred.
:type error: BaseException
:param kwargs: Additional keyword arguments.
response (LLMResult): The response which was generated beforethe error occurred.
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Print out the token.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
Print out the prompts.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever ends running.
Run when Retriever ends running.
on_retriever_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
on_retry(retry_state: RetryCallState, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on a retry event.
on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on arbitrary text.
on_tool_end(output: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when tool ends running.
on_tool_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when tool errors.
on_tool_start(serialized: Dict[str, Any], input_str: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when tool starts running.
langchain_core.callbacks.manager.handle_event¶
langchain_core.callbacks.manager.handle_event(handlers: List[BaseCallbackHandler], event_name: str, ignore_condition_name: Optional[str], *args: Any, **kwargs: Any) → None[source]¶
Generic event handler for CallbackManager.
Note: This function is used by langserve to handle events.
Parameters
handlers – The list of handlers that will handle the event
event_name – The name of the event (e.g., “on_llm_start”)
ignore_condition_name – Name of the attribute defined on handler
that if True will cause the handler to be skipped for the given event
*args – The arguments to pass to the event handler
**kwargs – The keyword arguments to pass to the event handler
langchain_core.callbacks.manager.CallbackManagerForLLMRun¶
class langchain_core.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, metadata: Optional[Dict[str, Any]] = None, inheritable_metadata: Optional[Dict[str, Any]] = None)[source]¶
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.
metadata (Optional[Dict[str, Any]]) – The metadata.
inheritable_metadata (Optional[Dict[str, Any]]) – The inheritable metadata.
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, *[, chunk])
Run when LLM generates a new token.
on_retry(retry_state, **kwargs)
Run on a retry event.
on_text(text, **kwargs)
Run when text is received.
on_text(text, **kwargs)
Run when text is received.
__init__(*, 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, metadata: Optional[Dict[str, Any]] = None, inheritable_metadata: Optional[Dict[str, Any]] = None) → None¶
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.
metadata (Optional[Dict[str, Any]]) – The metadata.
inheritable_metadata (Optional[Dict[str, Any]]) – The inheritable metadata.
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
response (LLMResult) – The LLM result.
on_llm_error(error: BaseException, **kwargs: Any) → None[source]¶
Run when LLM errors.
Parameters
error (Exception or KeyboardInterrupt) – The error.
kwargs (Any) – Additional keyword arguments.
- response (LLMResult): The response which was generated before
the error occurred.
- response (LLMResult): The response which was generated before
the error occurred.
on_llm_new_token(token: str, *, chunk: Optional[Union[GenerationChunk, ChatGenerationChunk]] = None, **kwargs: Any) → None[source]¶
Run when LLM generates a new token.
Parameters
token (str) – The new token.
on_retry(retry_state: RetryCallState, **kwargs: Any) → None¶
Run on a retry event.
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
Examples using CallbackManagerForLLMRun¶
Custom LLM