Tony20100/langchain_full huggingface dataset:
langchain_community.callbacks.streamlit.mutable_expander.MutableExpander¶
class langchain_community.callbacks.streamlit.mutable_expander.MutableExpander(parent_container: DeltaGenerator, label: str, expanded: bool)[source]¶
Streamlit expander that can be renamed and dynamically expanded/collapsed.
Create a new MutableExpander.
Parameters
parent_container (DeltaGenerator) – The st.container that the expander will be created inside.
The expander transparently deletes and recreates its underlying
st.expander instance when its label changes, and it uses
parent_container to ensure it recreates this underlying expander in the
same location onscreen.
label (str) – The expander’s initial label.
expanded (bool) – The expander’s initial expanded value.
Attributes
expanded
True if the expander was created with expanded=True.
label
Expander's label string.
Methods
__init__(parent_container, label, expanded)
Create a new MutableExpander.
append_copy(other)
Append a copy of another MutableExpander's children to this MutableExpander.
clear()
Remove the container and its contents entirely.
exception(exception, *[, index])
Add an Exception element to the container and return its index.
markdown(body[, unsafe_allow_html, help, index])
Add a Markdown element to the container and return its index.
update(*[, new_label, new_expanded])
Change the expander's label and expanded state
__init__(parent_container: DeltaGenerator, label: str, expanded: bool)[source]¶
Create a new MutableExpander.
Parameters
parent_container (DeltaGenerator) – The st.container that the expander will be created inside.
The expander transparently deletes and recreates its underlying
st.expander instance when its label changes, and it uses
st.expander instance when its label changes, and it uses
parent_container to ensure it recreates this underlying expander in the
same location onscreen.
label (str) – The expander’s initial label.
expanded (bool) – The expander’s initial expanded value.
append_copy(other: MutableExpander) → None[source]¶
Append a copy of another MutableExpander’s children to this
MutableExpander.
Parameters
other (MutableExpander) – 
Return type
None
clear() → None[source]¶
Remove the container and its contents entirely. A cleared container can’t
be reused.
Return type
None
exception(exception: BaseException, *, index: Optional[int] = None) → int[source]¶
Add an Exception element to the container and return its index.
Parameters
exception (BaseException) – 
index (Optional[int]) – 
Return type
int
markdown(body: SupportsStr, unsafe_allow_html: bool = False, *, help: Optional[str] = None, index: Optional[int] = None) → int[source]¶
Add a Markdown element to the container and return its index.
Parameters
body (SupportsStr) – 
unsafe_allow_html (bool) – 
help (Optional[str]) – 
index (Optional[int]) – 
Return type
int
update(*, new_label: Optional[str] = None, new_expanded: Optional[bool] = None) → None[source]¶
Change the expander’s label and expanded state
Parameters
new_label (Optional[str]) – 
new_expanded (Optional[bool]) – 
Return type
None
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__(job_type: Optional[str] = None, project: Optional[str] = 'langchain_callback_demo', entity: Optional[str] = None, tags: Optional[Sequence] = None, group: Optional[str] = None, name: Optional[str] = None, notes: Optional[str] = None, visualize: bool = False, complexity_metrics: bool = False, stream_logs: bool = False) → None[source]¶
Initialize callback handler.
Parameters
job_type (Optional[str]) – 
project (Optional[str]) – 
entity (Optional[str]) – 
tags (Optional[Sequence]) – 
group (Optional[str]) – 
name (Optional[str]) – 
notes (Optional[str]) – 
visualize (bool) – 
complexity_metrics (bool) – 
stream_logs (bool) – 
Return type
None
flush_tracker(langchain_asset: Optional[Any] = None, reset: bool = True, finish: bool = False, job_type: Optional[str] = None, project: Optional[str] = None, entity: Optional[str] = None, tags: Optional[Sequence] = None, group: Optional[str] = None, name: Optional[str] = None, notes: Optional[str] = None, visualize: Optional[bool] = None, complexity_metrics: Optional[bool] = None) → None[source]¶
Flush the tracker and reset the session.
Parameters
Flush the tracker and reset the session.
Parameters
langchain_asset (Optional[Any]) – The langchain asset to save.
reset (bool) – Whether to reset the session.
finish (bool) – Whether to finish the run.
job_type (Optional[str]) – The job type.
project (Optional[str]) – The project.
entity (Optional[str]) – The entity.
tags (Optional[Sequence]) – The tags.
group (Optional[str]) – The group.
name (Optional[str]) – The name.
notes (Optional[str]) – The notes.
visualize (Optional[bool]) – Whether to visualize.
complexity_metrics (Optional[bool]) – Whether to compute complexity metrics.
Returns – None
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: Dict[str, Any], **kwargs: Any) → None[source]¶
Run when chain ends running.
Parameters
outputs (Dict[str, Any]) – 
kwargs (Any) – 
Return type
None
on_chain_error(error: BaseException, **kwargs: Any) → None[source]¶
Run when chain errors.
Parameters
error (BaseException) – 
kwargs (Any) – 
Return type
None
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]¶
Run when agent is ending.
Parameters
text (str) – 
kwargs (Any) – 
Return type
None
on_tool_end(output: Any, **kwargs: Any) → None[source]¶
Run when tool ends running.
Parameters
output (Any) – 
kwargs (Any) – 
Return type
None
on_tool_error(error: BaseException, **kwargs: Any) → None[source]¶
Run when tool errors.
Parameters
error (BaseException) – 
kwargs (Any) – 
Return type
None
on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None[source]¶
Run when tool starts running.
Parameters
serialized (Dict[str, Any]) – 
input_str (str) – 
kwargs (Any) – 
Return type
None
reset_callback_meta() → None¶
Reset the callback metadata.
Return type
None
Examples using WandbCallbackHandler¶
Weights & Biases
langchain_nvidia_ai_endpoints.callbacks.standardize_model_name¶
langchain_nvidia_ai_endpoints.callbacks.standardize_model_name(model_name: str, price_map: dict = {}, is_completion: bool = False) → str[source]¶
Standardize the model name to a format that can be used in the OpenAI API.
Parameters
model_name (str) – Model name to standardize.
is_completion (bool) – Whether the model is used for completion or not.
Defaults to False.
price_map (dict) – 
Returns
Standardized model name.
Return type
str
langchain_community.callbacks.streamlit.streamlit_callback_handler.StreamlitCallbackHandler¶
class langchain_community.callbacks.streamlit.streamlit_callback_handler.StreamlitCallbackHandler(parent_container: DeltaGenerator, *, max_thought_containers: int = 4, expand_new_thoughts: bool = True, collapse_completed_thoughts: bool = True, thought_labeler: Optional[LLMThoughtLabeler] = None)[source]¶
Callback handler that writes to a Streamlit app.
Create a StreamlitCallbackHandler instance.
Parameters
parent_container (DeltaGenerator) – The st.container that will contain all the Streamlit elements that the
Handler creates.
max_thought_containers (int) – The max number of completed LLM thought containers to show at once. When
this threshold is reached, a new thought will cause the oldest thoughts to
be collapsed into a “History” expander. Defaults to 4.
expand_new_thoughts (bool) – Each LLM “thought” gets its own st.expander. This param controls whether
that expander is expanded by default. Defaults to True.
collapse_completed_thoughts (bool) – If True, LLM thought expanders will be collapsed when completed.
Defaults to True.
thought_labeler (Optional[LLMThoughtLabeler]) – An optional custom LLMThoughtLabeler instance. If unspecified, the handler
will use the default thought labeling logic. Defaults to None.
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__(parent_container, *[, ...])
Create a StreamlitCallbackHandler instance.
Create a StreamlitCallbackHandler instance.
on_agent_action(action[, color])
Run on agent action.
on_agent_finish(finish[, color])
Run on agent end.
on_chain_end(outputs, **kwargs)
Run when chain ends running.
on_chain_error(error, **kwargs)
Run when chain errors.
on_chain_start(serialized, inputs, **kwargs)
Run when chain starts running.
on_chat_model_start(serialized, messages, *, ...)
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. :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)
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[, color, end])
Run on arbitrary text.
on_tool_end(output[, color, ...])
Run when tool ends running.
on_tool_error(error, **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.
__init__(parent_container: DeltaGenerator, *, max_thought_containers: int = 4, expand_new_thoughts: bool = True, collapse_completed_thoughts: bool = True, thought_labeler: Optional[LLMThoughtLabeler] = None)[source]¶
Create a StreamlitCallbackHandler instance.
Parameters
parent_container (DeltaGenerator) – The st.container that will contain all the Streamlit elements that the
Handler creates.
max_thought_containers (int) – The max number of completed LLM thought containers to show at once. When
this threshold is reached, a new thought will cause the oldest thoughts to
be collapsed into a “History” expander. Defaults to 4.
expand_new_thoughts (bool) – Each LLM “thought” gets its own st.expander. This param controls whether
that expander is expanded by default. Defaults to True.
collapse_completed_thoughts (bool) – If True, LLM thought expanders will be collapsed when completed.
Defaults to True.
thought_labeler (Optional[LLMThoughtLabeler]) – An optional custom LLMThoughtLabeler instance. If unspecified, the handler
will use the default thought labeling logic. Defaults to 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
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]¶
Run when chain ends running.
Parameters
outputs (Dict[str, Any]) – 
kwargs (Any) – 
Return type
None
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
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.
: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) – 
kwargs (Any) – 
Return type
None
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
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) – 
kwargs (Any) – 
Return type
None
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
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 on arbitrary text.
Parameters
text (str) – 
color (Optional[str]) – 
end (str) – 
kwargs (Any) – 
Return type
None
on_tool_end(output: Any, color: Optional[str] = None, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any) → None[source]¶
Run when tool ends running.
Parameters
output (Any) – 
color (Optional[str]) – 
observation_prefix (Optional[str]) – 
llm_prefix (Optional[str]) – 
kwargs (Any) – 
Return type
None
on_tool_error(error: BaseException, **kwargs: Any) → None[source]¶
Run when tool errors.
Parameters
error (BaseException) – 
kwargs (Any) – 
Return type
None
on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None[source]¶
Run when tool starts running.
Parameters
serialized (Dict[str, Any]) – 
input_str (str) – 
kwargs (Any) – 
Return type
None
Examples using StreamlitCallbackHandler¶
GPT4All
Streamlit
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, *, 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, *, 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.
Parameters
pl_id_callback (Optional[Callable[..., Any]]) – 
pl_tags (Optional[List[str]]) – 
__init__(pl_id_callback: Optional[Callable[[...], Any]] = None, pl_tags: Optional[List[str]] = None) → None[source]¶
Initialize the PromptLayerCallbackHandler.
Parameters
Initialize the PromptLayerCallbackHandler.
Parameters
pl_id_callback (Optional[Callable[[...], Any]]) – 
pl_tags (Optional[List[str]]) – 
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]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → Any[source]¶
Run when a chat model starts running.
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]]) – 
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]) –
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¶
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
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, **kwargs: Any) → Any[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
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]]) – 
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
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 PromptLayerCallbackHandler¶
PromptLayer