Tony20100/langchain_full huggingface dataset:
langchain_core.callbacks.base.AsyncCallbackHandler¶
class langchain_core.callbacks.base.AsyncCallbackHandler[source]¶
Async callback handler that handles callbacks from LangChain.
Attributes
ignore_agent
Whether to ignore agent callbacks.
ignore_chain
Whether to ignore chain callbacks.
ignore_chat_model
Whether to ignore chat model callbacks.
ignore_llm
Whether to ignore LLM callbacks.
ignore_retriever
Whether to ignore retriever callbacks.
ignore_retry
Whether to ignore retry callbacks.
raise_error
run_inline
Methods
__init__()
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[, ...])
Run when chain ends running.
on_chain_error(error, *, 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[, ...])
Run when LLM ends running.
on_llm_error(error, *, run_id[, ...])
Run when LLM errors.
on_llm_new_token(token, *[, chunk, ...])
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.
Run on retriever error.
on_retriever_start(serialized, query, *, run_id)
Run on retriever start.
on_retry(retry_state, *, run_id[, parent_run_id])
Run on a retry event.
on_text(text, *, run_id[, parent_run_id, tags])
Run on arbitrary text.
on_tool_end(output, *, run_id[, ...])
Run when tool ends running.
on_tool_error(error, *, run_id[, ...])
Run when tool errors.
on_tool_start(serialized, input_str, *, run_id)
Run when tool starts running.
__init__()¶
async on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None[source]¶
Run on agent action.
Parameters
action (AgentAction) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
tags (Optional[List[str]]) – 
kwargs (Any) – 
Return type
None
async on_agent_finish(finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None[source]¶
Run on agent end.
Parameters
finish (AgentFinish) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
tags (Optional[List[str]]) – 
kwargs (Any) – 
Return type
None
kwargs (Any) – 
Return type
None
async on_chain_end(outputs: Dict[str, Any], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None[source]¶
Run when chain ends running.
Parameters
outputs (Dict[str, Any]) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
tags (Optional[List[str]]) – 
kwargs (Any) – 
Return type
None
async on_chain_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None[source]¶
Run when chain errors.
Parameters
error (BaseException) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
tags (Optional[List[str]]) – 
kwargs (Any) – 
Return type
None
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, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None[source]¶
Run when chain starts running.
Parameters
serialized (Dict[str, Any]) – 
inputs (Dict[str, Any]) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
tags (Optional[List[str]]) – 
metadata (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Return type
None
kwargs (Any) – 
Return type
None
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, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any[source]¶
Run when a chat model starts running.
ATTENTION: This method is called for chat models. If you’re implementinga handler for a non-chat model, you should use on_llm_start instead.
Parameters
serialized (Dict[str, Any]) – 
messages (List[List[BaseMessage]]) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
tags (Optional[List[str]]) – 
metadata (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Return type
Any
async on_llm_end(response: LLMResult, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None[source]¶
Run when LLM ends running.
Parameters
response (LLMResult) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
tags (Optional[List[str]]) – 
kwargs (Any) – 
Return type
None
async on_llm_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None[source]¶
Run when LLM errors.
Parameters
error (BaseException) – The error that occurred.
kwargs (Any) – Additional keyword arguments.
- response (LLMResult): The response which was generated before
- response (LLMResult): The response which was generated before
the error occurred.
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
tags (Optional[List[str]]) – 
Return type
None
async on_llm_new_token(token: str, *, chunk: Optional[Union[GenerationChunk, ChatGenerationChunk]] = None, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None[source]¶
Run on new LLM token. Only available when streaming is enabled.
Parameters
token (str) – 
chunk (Optional[Union[GenerationChunk, ChatGenerationChunk]]) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
tags (Optional[List[str]]) – 
kwargs (Any) – 
Return type
None
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, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None[source]¶
Run when LLM starts running.
ATTENTION: This method is called for non-chat models (regular LLMs). Ifyou’re implementing a handler for a chat model,
you should use on_chat_model_start instead.
Parameters
serialized (Dict[str, Any]) – 
prompts (List[str]) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
tags (Optional[List[str]]) – 
metadata (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Return type
None
kwargs (Any) – 
Return type
None
async on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None[source]¶
Run on retriever end.
Parameters
documents (Sequence[Document]) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
tags (Optional[List[str]]) – 
kwargs (Any) – 
Return type
None
async on_retriever_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None[source]¶
Run on retriever error.
Parameters
error (BaseException) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
tags (Optional[List[str]]) – 
kwargs (Any) – 
Return type
None
async 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) → None[source]¶
Run on retriever start.
Parameters
serialized (Dict[str, Any]) – 
query (str) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
tags (Optional[List[str]]) – 
metadata (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Return type
None
kwargs (Any) – 
Return type
None
async on_retry(retry_state: RetryCallState, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any[source]¶
Run on a retry event.
Parameters
retry_state (RetryCallState) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
async on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None[source]¶
Run on arbitrary text.
Parameters
text (str) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
tags (Optional[List[str]]) – 
kwargs (Any) – 
Return type
None
async on_tool_end(output: Any, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None[source]¶
Run when tool ends running.
Parameters
output (Any) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
tags (Optional[List[str]]) – 
kwargs (Any) – 
Return type
None
async on_tool_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None[source]¶
Run when tool errors.
Parameters
error (BaseException) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
tags (Optional[List[str]]) – 
kwargs (Any) –
tags (Optional[List[str]]) – 
kwargs (Any) – 
Return type
None
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, metadata: Optional[Dict[str, Any]] = None, inputs: Optional[Dict[str, Any]] = None, **kwargs: Any) → None[source]¶
Run when tool starts running.
Parameters
serialized (Dict[str, Any]) – 
input_str (str) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
tags (Optional[List[str]]) – 
metadata (Optional[Dict[str, Any]]) – 
inputs (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Return type
None
Examples using AsyncCallbackHandler¶
Bedrock
How to use callbacks in async environments
langchain_community.callbacks.labelstudio_callback.get_default_label_configs¶
langchain_community.callbacks.labelstudio_callback.get_default_label_configs(mode: Union[str, LabelStudioMode]) → Tuple[str, LabelStudioMode][source]¶
Get default Label Studio configs for the given mode.
Parameters
mode (Union[str, LabelStudioMode]) – Label Studio mode (“prompt” or “chat”)
Return type
Tuple[str, LabelStudioMode]
Returns: Tuple of Label Studio config and mode
langchain_core.callbacks.manager.ahandle_event¶
async langchain_core.callbacks.manager.ahandle_event(handlers: List[BaseCallbackHandler], event_name: str, ignore_condition_name: Optional[str], *args: Any, **kwargs: Any) → None[source]¶
Generic event handler for AsyncCallbackManager.
Note: This function is used by langserve to handle events.
Parameters
handlers (List[BaseCallbackHandler]) – The list of handlers that will handle the event
event_name (str) – The name of the event (e.g., “on_llm_start”)
ignore_condition_name (Optional[str]) – Name of the attribute defined on handler
that if True will cause the handler to be skipped for the given event
*args (Any) – The arguments to pass to the event handler
**kwargs (Any) – The keyword arguments to pass to the event handler
Return type
None
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.
Return type
None
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.
Return type
BaseRunManager
on_retry(retry_state: RetryCallState, **kwargs: Any) → None¶
Run on a retry event.
Parameters
retry_state (RetryCallState) – 
kwargs (Any) – 
Return type
None
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, metadata: Optional[Dict[str, Any]] = 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.
metadata (Dict[str, Any], optional) – The metadata to apply to all runs.
Defaults to None.
Return type
Generator[CallbackManagerForChainGroup, None, 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
Parameters
group_name (str) – 
callback_manager (Optional[CallbackManager]) –
group_name (str) – 
callback_manager (Optional[CallbackManager]) – 
inputs (Optional[Dict[str, Any]]) – 
project_name (Optional[str]) – 
example_id (Optional[Union[str, UUID]]) – 
run_id (Optional[UUID]) – 
tags (Optional[List[str]]) – 
metadata (Optional[Dict[str, Any]]) – 
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.invoke(llm_input, {"callbacks": manager})
    manager.on_chain_end({"output": res})
langchain_community.callbacks.upstash_ratelimit_callback.UpstashRatelimitError¶
class langchain_community.callbacks.upstash_ratelimit_callback.UpstashRatelimitError(message: str, type: Literal['token', 'request'], limit: Optional[int] = None, reset: Optional[float] = None)[source]¶
Upstash Ratelimit Error
Raised when the rate limit is reached in UpstashRatelimitHandler
Parameters
message (str) – error message
type (str) – The kind of the limit which was reached. One of
“token” or “request”
limit (Optional[int]) – The limit which was reached. Passed when type
is request
reset (Optional[int]) – unix timestamp in milliseconds when the limits
are reset. Passed when type is request
langchain_community.callbacks.confident_callback.DeepEvalCallbackHandler¶
class langchain_community.callbacks.confident_callback.DeepEvalCallbackHandler(metrics: List[Any], implementation_name: Optional[str] = None)[source]¶
Callback Handler that logs into deepeval.
Parameters
implementation_name (Optional[str]) – name of the implementation in deepeval
metrics (List[Any]) – A list of metrics
Raises
ImportError – if the deepeval package is not installed.
Examples
>>> from langchain_community.llms import OpenAI
>>> from langchain_community.callbacks import DeepEvalCallbackHandler
>>> from deepeval.metrics import AnswerRelevancy
>>> metric = AnswerRelevancy(minimum_score=0.3)
>>> deepeval_callback = DeepEvalCallbackHandler(
...     implementation_name="exampleImplementation",
...     metrics=[metric],
... )
>>> llm = OpenAI(
...     temperature=0,
...     callbacks=[deepeval_callback],
...     verbose=True,
...     openai_api_key="API_KEY_HERE",
... )
>>> llm.generate([
...     "What is the best evaluation tool out there? (no bias at all)",
... ])
"Deepeval, no doubt about it."
Initializes the deepevalCallbackHandler.
Parameters
implementation_name (Optional[str]) – Name of the implementation you want.
metrics (List[Any]) – What metrics do you want to track?
Raises
ImportError – if the deepeval package is not installed.
ConnectionError – if the connection to deepeval fails.
Attributes
BLOG_URL
ISSUES_URL
REPO_URL
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.
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__(metrics[, implementation_name])
Initializes the deepevalCallbackHandler.
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 chain ends.
on_chain_error(error, **kwargs)
Do nothing when LLM chain outputs an error.
on_chain_start(serialized, inputs, **kwargs)
Do nothing when chain starts
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, **kwargs)
Log records to deepeval 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)
Store 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, **kwargs)
Do nothing
Run on a retry event.
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.
__init__(metrics: List[Any], implementation_name: Optional[str] = None) → None[source]¶
Initializes the deepevalCallbackHandler.
Parameters
implementation_name (Optional[str]) – Name of the implementation you want.
metrics (List[Any]) – What metrics do you want to track?
Raises
ImportError – if the deepeval package is not installed.
ConnectionError – if the connection to deepeval fails.
Return type
None
on_agent_action(action: AgentAction, **kwargs: Any) → Any[source]¶
Do nothing when agent takes a specific action.
Parameters
action (AgentAction) – 
kwargs (Any) – 
Return type
Any
on_agent_finish(finish: AgentFinish, **kwargs: Any) → None[source]¶
Do nothing
Parameters
finish (AgentFinish) – 
kwargs (Any) – 
Return type
None
on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Do nothing when chain ends.
Parameters
outputs (Dict[str, Any]) – 
kwargs (Any) – 
Return type
None
on_chain_error(error: BaseException, **kwargs: Any) → None[source]¶
Do nothing when LLM chain outputs an error.
Parameters
error (BaseException) – 
kwargs (Any) – 
Return type
None
error (BaseException) – 
kwargs (Any) – 
Return type
None
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Do nothing when chain starts
Parameters
serialized (Dict[str, Any]) – 
inputs (Dict[str, Any]) – 
kwargs (Any) – 
Return type
None
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
ATTENTION: This method is called for chat models. If you’re implementinga handler for a non-chat model, you should use on_llm_start instead.
Parameters
serialized (Dict[str, Any]) – 
messages (List[List[BaseMessage]]) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
tags (Optional[List[str]]) – 
metadata (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Return type
Any
on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
Log records to deepeval when an LLM ends.
Parameters
response (LLMResult) – 
kwargs (Any) – 
Return type
None
on_llm_error(error: BaseException, **kwargs: Any) → None[source]¶
Do nothing when LLM outputs an error.
Parameters
error (BaseException) – 
kwargs (Any) – 
Return type
None
error (BaseException) – 
kwargs (Any) – 
Return type
None
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Do nothing when a new token is generated.
Parameters
token (str) – 
kwargs (Any) – 
Return type
None
on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
Store the prompts
Parameters
serialized (Dict[str, Any]) – 
prompts (List[str]) – 
kwargs (Any) – 
Return type
None
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever ends running.
Parameters
documents (Sequence[Document]) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
on_retriever_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
Parameters
error (BaseException) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
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.
Parameters
serialized (Dict[str, Any]) –
Parameters
serialized (Dict[str, Any]) – 
query (str) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
tags (Optional[List[str]]) – 
metadata (Optional[Dict[str, Any]]) – 
kwargs (Any) – 
Return type
Any
on_retry(retry_state: RetryCallState, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on a retry event.
Parameters
retry_state (RetryCallState) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
on_text(text: str, **kwargs: Any) → None[source]¶
Do nothing
Parameters
text (str) – 
kwargs (Any) – 
Return type
None
on_tool_end(output: Any, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any) → None[source]¶
Do nothing when tool ends.
Parameters
output (Any) – 
observation_prefix (Optional[str]) – 
llm_prefix (Optional[str]) – 
kwargs (Any) – 
Return type
None
on_tool_error(error: BaseException, **kwargs: Any) → None[source]¶
Do nothing when tool outputs an error.
Parameters
error (BaseException) – 
kwargs (Any) – 
Return type
None
on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None[source]¶
Do nothing when tool starts.
Parameters
serialized (Dict[str, Any]) – 
input_str (str) – 
kwargs (Any) – 
Return type
None
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 (List[BaseCallbackHandler]) – The list of handlers that will handle the event
event_name (str) – The name of the event (e.g., “on_llm_start”)
ignore_condition_name (Optional[str]) – Name of the attribute defined on handler
that if True will cause the handler to be skipped for the given event
*args (Any) – The arguments to pass to the event handler
**kwargs (Any) – The keyword arguments to pass to the event handler
Return type
None