Tony20100/langchain_full huggingface dataset:
langchain_core.callbacks.file.FileCallbackHandler¶
class langchain_core.callbacks.file.FileCallbackHandler(filename: str, mode: str = 'a', color: Optional[str] = None)[source]¶
Callback Handler that writes to a file.
Initialize callback handler.
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__(filename[, mode, color])
Initialize callback handler.
on_agent_action(action[, color])
Run on agent action.
on_agent_finish(finish[, color])
Run on agent end.
on_chain_end(outputs, **kwargs)
Print out that we finished a chain.
on_chain_error(error, *, run_id[, parent_run_id])
Run when chain errors.
on_chain_start(serialized, inputs, **kwargs)
Print out that we are entering a chain.
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. :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.
Run on new LLM token.
on_llm_start(serialized, prompts, *, run_id)
Run when LLM starts running.
on_retriever_end(documents, *, run_id[, ...])
Run when Retriever ends running.
on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_retry(retry_state, *, run_id[, parent_run_id])
Run on a retry event.
on_text(text[, color, end])
Run when agent ends.
on_tool_end(output[, color, ...])
If not the final action, print out observation.
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.
Parameters
filename (str) – 
mode (str) – 
color (Optional[str]) – 
__init__(filename: str, mode: str = 'a', color: Optional[str] = None) → None[source]¶
Initialize callback handler.
Parameters
filename (str) – 
mode (str) – 
color (Optional[str]) – 
Return type
None
on_agent_action(action: AgentAction, color: Optional[str] = None, **kwargs: Any) → Any[source]¶
Run on agent action.
Parameters
action (AgentAction) – 
color (Optional[str]) – 
kwargs (Any) – 
Return type
Any
color (Optional[str]) – 
kwargs (Any) – 
Return type
Any
on_agent_finish(finish: AgentFinish, color: Optional[str] = None, **kwargs: Any) → None[source]¶
Run on agent end.
Parameters
finish (AgentFinish) – 
color (Optional[str]) – 
kwargs (Any) – 
Return type
None
on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Print out that we finished a chain.
Parameters
outputs (Dict[str, Any]) – 
kwargs (Any) – 
Return type
None
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
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Print out that we are entering a chain.
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.
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, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when LLM ends running.
Parameters
response (LLMResult) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
Any
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.
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
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], *, 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 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
Any
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]]) – 
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, color: Optional[str] = None, end: str = '', **kwargs: Any) → None[source]¶
Run when agent ends.
Parameters
text (str) – 
color (Optional[str]) – 
end (str) – 
kwargs (Any) – 
Return type
None
on_tool_end(output: str, color: Optional[str] = None, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any) → None[source]¶
If not the final action, print out observation.
Parameters
output (str) – 
color (Optional[str]) – 
observation_prefix (Optional[str]) – 
llm_prefix (Optional[str]) – 
kwargs (Any) – 
Return type
None
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
langchain_community.callbacks.llmonitor_callback.identify¶
langchain_community.callbacks.llmonitor_callback.identify(user_id: str, user_props: Optional[Any] = None) → UserContextManager[source]¶
Builds an LLMonitor UserContextManager
Parameters
user_id (-) – The user id.
user_props (-) – The user properties.
Returns
A context manager that sets the user context.
Return type
UserContextManager
Examples using identify¶
LLMonitor
langchain_community.callbacks.llmonitor_callback.LLMonitorCallbackHandler¶
class langchain_community.callbacks.llmonitor_callback.LLMonitorCallbackHandler(app_id: Optional[str] = None, api_url: Optional[str] = None, verbose: bool = False)[source]¶
Callback Handler for LLMonitor`.
#### Parameters:
app_id: The app id of the app you want to report to. Defaults to
None, which means that LLMONITOR_APP_ID will be used.
- api_url: The url of the LLMonitor API. Defaults to None,
which means that either LLMONITOR_API_URL environment variable
or https://app.llmonitor.com will be used.
#### Raises:
ValueError: if app_id is not provided either as an
argument or as an environment variable.
- ConnectionError: if the connection to the API fails.
#### Example:
```python
from langchain_community.llms import OpenAI
from langchain_community.callbacks import LLMonitorCallbackHandler
llmonitor_callback = LLMonitorCallbackHandler()
llm = OpenAI(callbacks=[llmonitor_callback],
metadata={“userId”: “user-123”})
llm.invoke(“Hello, how are you?”)
```
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__([app_id, api_url, verbose])
on_agent_action(action, *, run_id[, ...])
Run on agent action.
on_agent_finish(finish, *, run_id[, ...])
Run on agent end.
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.
Parameters
app_id (Optional[str]) – 
api_url (Optional[str]) – 
verbose (bool) – 
__init__(app_id: Optional[str] = None, api_url: Optional[str] = None, verbose: bool = False) → None[source]¶
Parameters
app_id (Optional[str]) – 
api_url (Optional[str]) – 
verbose (bool) – 
Return type
None
on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any[source]¶
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[source]¶
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[source]¶
Run when chain ends running.
Parameters
outputs (Dict[str, Any]) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
kwargs (Any) – 
Return type
Any
on_chain_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any[source]¶
Run when chain errors.
Parameters
error (BaseException) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
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[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
Any
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]) –
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, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None[source]¶
Run when LLM ends running.
Parameters
response (LLMResult) – 
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
kwargs (Any) – 
Return type
None
on_llm_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any[source]¶
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.
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
kwargs (Any) – 
Return type
Any
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
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
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_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, 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]) –
run_id (UUID) – 
parent_run_id (Optional[UUID]) – 
tags (Optional[List[str]]) – 
kwargs (Any) – 
Return type
None
on_tool_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any[source]¶
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, **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]]) – 
kwargs (Any) – 
Return type
None
Examples using LLMonitorCallbackHandler¶
LLMonitor
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, run_id: Optional[str] = None, artifacts_dir: str = '')[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.
run_id (Optional[str]) – 
artifacts_dir (str) – 
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, ...])
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_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, **kwargs)
Run when Retriever ends running.
on_retriever_error(error, **kwargs)
Run when Retriever errors.
on_retriever_start(serialized, query, **kwargs)
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 text is received.
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.
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, run_id: Optional[str] = None, artifacts_dir: str = '') → None[source]¶
Initialize callback handler.
Parameters
name (Optional[str]) – 
experiment (Optional[str]) – 
tags (Optional[Dict]) – 
tracking_uri (Optional[str]) – 
run_id (Optional[str]) – 
artifacts_dir (str) – 
Return type
None
flush_tracker(langchain_asset: Optional[Any] = None, finish: bool = False) → None[source]¶
Parameters
langchain_asset (Optional[Any]) – 
finish (bool) – 
Return type
None
get_custom_callback_meta() → Dict[str, Any]¶
Return type
Dict[str, Any]
on_agent_action(action: AgentAction, **kwargs: Any) → Any[source]¶
Run on agent action.
Parameters
action (AgentAction) – 
kwargs (Any) – 
Return type
Any
on_agent_finish(finish: AgentFinish, **kwargs: Any) → None[source]¶
Run when agent ends running.
Parameters
finish (AgentFinish) – 
kwargs (Any) – 
Return type
None
on_chain_end(outputs: Union[Dict[str, Any], str, List[str]], **kwargs: Any) → None[source]¶
Run when chain ends running.
Parameters
outputs (Union[Dict[str, Any], str, List[str]]) – 
kwargs (Any) – 
Return type
None
on_chain_error(error: BaseException, **kwargs: Any) → None[source]¶
on_chain_error(error: BaseException, **kwargs: Any) → None[source]¶
Run when chain errors.
Parameters
error (BaseException) – 
kwargs (Any) – 
Return type
None
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Run when chain starts running.
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]¶
Run when LLM ends running.
Parameters
response (LLMResult) – 
kwargs (Any) – 
Return type
None
on_llm_error(error: BaseException, **kwargs: Any) → None[source]¶
Run when LLM errors.
Parameters
error (BaseException) – 
kwargs (Any) – 
Return type