Tony20100/langchain_full huggingface dataset:
langchain_community.callbacks.argilla_callback.ArgillaCallbackHandler¶
class langchain_community.callbacks.argilla_callback.ArgillaCallbackHandler(dataset_name: str, workspace_name: Optional[str] = None, api_url: Optional[str] = None, api_key: Optional[str] = None)[source]¶
Callback Handler that logs into Argilla.
Parameters
dataset_name (str) – 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/tutorials_and_integrations/integrations/use_argilla_callback_in_langchain.html.
workspace_name (Optional[str]) – 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 (Optional[str]) – 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 will be used.
api_key (Optional[str]) – API Key to connect to the Argilla Server. Defaults to None, which
means that either ARGILLA_API_KEY environment variable or the default
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_community.llms import OpenAI
>>> from langchain_community.callbacks import ArgillaCallbackHandler
>>> argilla_callback = ArgillaCallbackHandler(
...     dataset_name="my-dataset",
...     workspace_name="my-workspace",
...     api_url="http://localhost:6900",
...     api_key="argilla.apikey",
... )
...     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 (str) – 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/tutorials_and_integrations/integrations/use_argilla_callback_in_langchain.html.
workspace_name (Optional[str]) – 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 (Optional[str]) – 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 will be used.
api_key (Optional[str]) – API Key to connect to the Argilla Server. Defaults to None, which
means that either ARGILLA_API_KEY environment variable or the default
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.
Attributes
BLOG_URL
DEFAULT_API_URL
ISSUES_URL
REPO_URL
ignore_agent
Whether to ignore agent callbacks.
ignore_chain
Whether to ignore chain callbacks.
ignore_chat_model
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__(dataset_name[, workspace_name, ...])
Initializes the ArgillaCallbackHandler.
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[, ...])
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
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__(dataset_name: str, workspace_name: Optional[str] = None, api_url: Optional[str] = None, api_key: Optional[str] = None) → None[source]¶
Initializes the ArgillaCallbackHandler.
Parameters
dataset_name (str) – 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/tutorials_and_integrations/integrations/use_argilla_callback_in_langchain.html.
workspace_name (Optional[str]) – 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 (Optional[str]) – 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 will be used.
api_key (Optional[str]) – API Key to connect to the Argilla Server. Defaults to None, which
means that either ARGILLA_API_KEY environment variable or the default
means that either ARGILLA_API_KEY environment variable or the default
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.
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]¶
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.
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
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.
Parameters
when the 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 Argilla 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
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
token (str) – 
kwargs (Any) – 
Return type
None
on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
Save the prompts in memory when an LLM starts.
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]) – 
query (str) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
tags (Optional[List[str]]) – 
metadata (Optional[Dict[str, Any]]) –
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
Examples using ArgillaCallbackHandler¶
Argilla
langchain_community.callbacks.trubrics_callback.TrubricsCallbackHandler¶
class langchain_community.callbacks.trubrics_callback.TrubricsCallbackHandler(project: str = 'default', email: Optional[str] = None, password: Optional[str] = None, **kwargs: Any)[source]¶
Callback handler for Trubrics.
Parameters
project (str) – a trubrics project, default project is “default”
email (Optional[str]) – a trubrics account email, can equally be set in env variables
password (Optional[str]) – a trubrics account password, can equally be set in env variables
**kwargs (Any) – all other kwargs are parsed and set to trubrics prompt variables,
or added to the metadata dict
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__([project, email, password])
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, **kwargs)
on_llm_end(response, run_id, **kwargs)
Run when LLM ends running.
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, *[, chunk, ...])
Run on new LLM token.
on_llm_start(serialized, prompts, **kwargs)
Run when LLM starts running.
on_retriever_end(documents, *, run_id[, ...])
Run when Retriever ends running.
on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_retry(retry_state, *, run_id[, parent_run_id])
Run on a retry event.
on_text(text, *, 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__(project: str = 'default', email: Optional[str] = None, password: Optional[str] = None, **kwargs: Any) → None[source]¶
Parameters
project (str) – 
email (Optional[str]) –
Parameters
project (str) – 
email (Optional[str]) – 
password (Optional[str]) – 
kwargs (Any) – 
Return type
None
on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent action.
Parameters
action (AgentAction) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
on_agent_finish(finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent end.
Parameters
finish (AgentFinish) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
on_chain_end(outputs: Dict[str, Any], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when chain ends running.
Parameters
outputs (Dict[str, Any]) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
on_chain_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when chain errors.
Parameters
error (BaseException) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
kwargs (Any) – 
Return type
Any
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.
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
Any
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], **kwargs: Any) → None[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]]) – 
kwargs (Any) – 
Return type
None
on_llm_end(response: LLMResult, run_id: UUID, **kwargs: Any) → None[source]¶
Run when LLM ends running.
Parameters
response (LLMResult) – 
run_id (UUID) – 
kwargs (Any) – 
Return type
None
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.
:type error: BaseException
:param kwargs: Additional keyword arguments.
response (LLMResult): The response which was generated beforethe error occurred.
Parameters
error (BaseException) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
on_llm_new_token(token: str, *, chunk: Optional[Union[GenerationChunk, ChatGenerationChunk]] = None, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on new LLM token. Only available when streaming is enabled.
Parameters
token (str) – The new token.
chunk (GenerationChunk | ChatGenerationChunk) – The new generated chunk,
information. (containing content and other) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
on_llm_start(serialized: Dict[str, Any], prompts: List[str], **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]) – 
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]) –
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]) – 
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, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on arbitrary text.
Parameters
text (str) –
Run on arbitrary text.
Parameters
text (str) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
on_tool_end(output: Any, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when tool ends running.
Parameters
output (Any) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
on_tool_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when tool errors.
Parameters
error (BaseException) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
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) → Any¶
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
Any
Examples using TrubricsCallbackHandler¶
Trubrics
langchain_core.callbacks.manager.AsyncCallbackManagerForLLMRun¶
class langchain_core.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, metadata: Optional[Dict[str, Any]] = None, inheritable_metadata: Optional[Dict[str, Any]] = None)[source]¶
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.
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.
get_sync()
Get the equivalent sync RunManager.
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_retry(retry_state, **kwargs)
Run on a retry event.
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
classmethod get_noop_manager() → BRM¶
Return a manager that doesn’t perform any operations.
Returns
The noop manager.
Return type
BaseRunManager
get_sync() → CallbackManagerForLLMRun[source]¶
Get the equivalent sync RunManager.
Returns
The sync RunManager.
Return type
CallbackManagerForLLMRun
async on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
Run when LLM ends running.
Parameters
response (LLMResult) – The LLM result.
kwargs (Any) – 
Return type
None