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classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.schemas.TracerSessionV1.html
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1074b320e9eb-0
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langchain.callbacks.tracers.schemas.BaseRun¶
class langchain.callbacks.tracers.schemas.BaseRun[source]¶
Bases: BaseModel
Base class for Run.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param child_execution_order: int [Required]¶
param end_time: datetime.datetime [Optional]¶
param error: Optional[str] = None¶
param execution_order: int [Required]¶
param extra: Optional[Dict[str, Any]] = None¶
param parent_uuid: Optional[str] = None¶
param serialized: Dict[str, Any] [Required]¶
param session_id: int [Required]¶
param start_time: datetime.datetime [Optional]¶
param uuid: str [Required]¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.schemas.BaseRun.html
|
1074b320e9eb-1
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the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.schemas.BaseRun.html
|
1074b320e9eb-2
|
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.schemas.BaseRun.html
|
3a8d84c39e5a-0
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langchain.callbacks.wandb_callback.WandbCallbackHandler¶
class langchain.callbacks.wandb_callback.WandbCallbackHandler(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)[source]¶
Callback Handler that logs to Weights and Biases.
Parameters
job_type (str) – The type of job.
project (str) – The project to log to.
entity (str) – The entity to log to.
tags (list) – The tags to log.
group (str) – The group to log to.
name (str) – The name of the run.
notes (str) – The notes to log.
visualize (bool) – Whether to visualize the run.
complexity_metrics (bool) – Whether to log complexity metrics.
stream_logs (bool) – Whether to stream callback actions to W&B
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 using the run.log() method to Weights and Biases.
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
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.wandb_callback.WandbCallbackHandler.html
|
3a8d84c39e5a-1
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ignore_retriever
Whether to ignore retriever callbacks.
ignore_retry
Whether to ignore retry callbacks.
raise_error
run_inline
Methods
__init__([job_type, project, entity, tags, ...])
Initialize callback handler.
flush_tracker([langchain_asset, reset, ...])
Flush the tracker and reset the session.
get_custom_callback_meta()
on_agent_action(action, **kwargs)
Run on agent action.
on_agent_finish(finish, **kwargs)
Run when agent ends running.
on_chain_end(outputs, **kwargs)
Run when chain ends running.
on_chain_error(error, **kwargs)
Run when chain errors.
on_chain_start(serialized, inputs, **kwargs)
Run when chain starts running.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, **kwargs)
Run when LLM ends running.
on_llm_error(error, **kwargs)
Run when LLM errors.
on_llm_new_token(token, **kwargs)
Run when LLM generates a new token.
on_llm_start(serialized, prompts, **kwargs)
Run when LLM starts.
on_retriever_end(documents, *, run_id[, ...])
Run when Retriever ends running.
on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_text(text, **kwargs)
Run when agent is ending.
on_tool_end(output, **kwargs)
Run when tool ends running.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.wandb_callback.WandbCallbackHandler.html
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3a8d84c39e5a-2
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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.
flush_tracker(langchain_asset: 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
langchain_asset – The langchain asset to save.
reset – Whether to reset the session.
finish – Whether to finish the run.
job_type – The job type.
project – The project.
entity – The entity.
tags – The tags.
group – The group.
name – The name.
notes – The notes.
visualize – Whether to visualize.
complexity_metrics – Whether to compute complexity metrics.
Returns – None
get_custom_callback_meta() → Dict[str, Any]¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.wandb_callback.WandbCallbackHandler.html
|
3a8d84c39e5a-3
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Returns – None
get_custom_callback_meta() → Dict[str, Any]¶
on_agent_action(action: AgentAction, **kwargs: Any) → Any[source]¶
Run on agent action.
on_agent_finish(finish: AgentFinish, **kwargs: Any) → None[source]¶
Run when agent ends running.
on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Run when chain ends running.
on_chain_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when chain errors.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Run when chain starts running.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
Run when LLM ends running.
on_llm_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when LLM errors.
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Run when LLM generates a new token.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
Run when LLM starts.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.wandb_callback.WandbCallbackHandler.html
|
3a8d84c39e5a-4
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Run when Retriever ends running.
on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
on_text(text: str, **kwargs: Any) → None[source]¶
Run when agent is ending.
on_tool_end(output: str, **kwargs: Any) → None[source]¶
Run when tool ends running.
on_tool_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when tool errors.
on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None[source]¶
Run when tool starts running.
reset_callback_meta() → None¶
Reset the callback metadata.
Examples using WandbCallbackHandler¶
Weights & Biases
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.wandb_callback.WandbCallbackHandler.html
|
fab4af2531da-0
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langchain.callbacks.arize_callback.ArizeCallbackHandler¶
class langchain.callbacks.arize_callback.ArizeCallbackHandler(model_id: Optional[str] = None, model_version: Optional[str] = None, SPACE_KEY: Optional[str] = None, API_KEY: Optional[str] = None)[source]¶
Callback Handler that logs to Arize.
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__([model_id, model_version, ...])
Initialize callback handler.
on_agent_action(action, **kwargs)
Do nothing.
on_agent_finish(finish, **kwargs)
Run on agent end.
on_chain_end(outputs, **kwargs)
Do nothing.
on_chain_error(error, **kwargs)
Do nothing.
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)
Do nothing.
on_llm_new_token(token, **kwargs)
Do nothing.
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[, ...])
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.arize_callback.ArizeCallbackHandler.html
|
fab4af2531da-1
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on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_text(text, **kwargs)
Run on arbitrary text.
on_tool_end(output[, observation_prefix, ...])
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__(model_id: Optional[str] = None, model_version: Optional[str] = None, SPACE_KEY: Optional[str] = None, API_KEY: Optional[str] = None) → None[source]¶
Initialize callback handler.
on_agent_action(action: AgentAction, **kwargs: Any) → Any[source]¶
Do nothing.
on_agent_finish(finish: AgentFinish, **kwargs: Any) → None[source]¶
Run on agent end.
on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Do nothing.
on_chain_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Do nothing.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Run when chain starts running.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.arize_callback.ArizeCallbackHandler.html
|
fab4af2531da-2
|
Run when a chat model starts running.
on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
Run when LLM ends running.
on_llm_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Do nothing.
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Do nothing.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
Run when LLM starts running.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever ends running.
on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
on_text(text: str, **kwargs: Any) → None[source]¶
Run on arbitrary text.
on_tool_end(output: str, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any) → None[source]¶
Run when tool ends running.
on_tool_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when tool errors.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.arize_callback.ArizeCallbackHandler.html
|
fab4af2531da-3
|
Run when tool errors.
on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None[source]¶
Run when tool starts running.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.arize_callback.ArizeCallbackHandler.html
|
02c107dff6a2-0
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langchain.callbacks.base.BaseCallbackManager¶
class langchain.callbacks.base.BaseCallbackManager(handlers: List[BaseCallbackHandler], inheritable_handlers: Optional[List[BaseCallbackHandler]] = None, parent_run_id: Optional[UUID] = None, *, tags: Optional[List[str]] = None, inheritable_tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, inheritable_metadata: Optional[Dict[str, Any]] = None)[source]¶
Base callback manager that handles callbacks from LangChain.
Initialize callback manager.
Attributes
is_async
Whether the callback manager is async.
Methods
__init__(handlers[, inheritable_handlers, ...])
Initialize callback manager.
add_handler(handler[, inherit])
Add a handler to the callback manager.
add_metadata(metadata[, inherit])
add_tags(tags[, inherit])
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_start(serialized, prompts, *, run_id)
Run when LLM starts running.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_tool_start(serialized, input_str, *, run_id)
Run when tool starts running.
remove_handler(handler)
Remove a handler from the callback manager.
remove_metadata(keys)
remove_tags(tags)
set_handler(handler[, inherit])
Set handler as the only handler on the callback manager.
set_handlers(handlers[, inherit])
Set handlers as the only handlers on the callback manager.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.base.BaseCallbackManager.html
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02c107dff6a2-1
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Set handlers as the only handlers on the callback manager.
__init__(handlers: List[BaseCallbackHandler], inheritable_handlers: Optional[List[BaseCallbackHandler]] = None, parent_run_id: Optional[UUID] = None, *, tags: Optional[List[str]] = None, inheritable_tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, inheritable_metadata: Optional[Dict[str, Any]] = None) → None[source]¶
Initialize callback manager.
add_handler(handler: BaseCallbackHandler, inherit: bool = True) → None[source]¶
Add a handler to the callback manager.
add_metadata(metadata: Dict[str, Any], inherit: bool = True) → None[source]¶
add_tags(tags: List[str], inherit: bool = True) → None[source]¶
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when chain starts running.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_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.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.base.BaseCallbackManager.html
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02c107dff6a2-2
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Run when LLM starts running.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
on_tool_start(serialized: Dict[str, Any], input_str: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when tool starts running.
remove_handler(handler: BaseCallbackHandler) → None[source]¶
Remove a handler from the callback manager.
remove_metadata(keys: List[str]) → None[source]¶
remove_tags(tags: List[str]) → None[source]¶
set_handler(handler: BaseCallbackHandler, inherit: bool = True) → None[source]¶
Set handler as the only handler on the callback manager.
set_handlers(handlers: List[BaseCallbackHandler], inherit: bool = True) → None[source]¶
Set handlers as the only handlers on the callback manager.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.base.BaseCallbackManager.html
|
0297a7f01038-0
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langchain.callbacks.manager.AsyncCallbackManagerForRetrieverRun¶
class langchain.callbacks.manager.AsyncCallbackManagerForRetrieverRun(*, 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 retriever 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_child([tag])
Get a child callback manager.
get_noop_manager()
Return a manager that doesn't perform any operations.
on_retriever_end(documents, **kwargs)
Run when retriever ends running.
on_retriever_error(error, **kwargs)
Run when retriever errors.
on_retry(retry_state, **kwargs)
on_text(text, **kwargs)
Run when text is received.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.AsyncCallbackManagerForRetrieverRun.html
|
0297a7f01038-1
|
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.
get_child(tag: Optional[str] = None) → AsyncCallbackManager¶
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
AsyncCallbackManager
classmethod get_noop_manager() → BRM¶
Return a manager that doesn’t perform any operations.
Returns
The noop manager.
Return type
BaseRunManager
async on_retriever_end(documents: Sequence[Document], **kwargs: Any) → None[source]¶
Run when retriever ends running.
async on_retriever_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.AsyncCallbackManagerForRetrieverRun.html
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0297a7f01038-2
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Run when retriever errors.
async on_retry(retry_state: RetryCallState, **kwargs: Any) → None¶
async on_text(text: str, **kwargs: Any) → Any¶
Run when text is received.
Parameters
text (str) – The received text.
Returns
The result of the callback.
Return type
Any
Examples using AsyncCallbackManagerForRetrieverRun¶
Retrieve as you generate with FLARE
FLARE
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.AsyncCallbackManagerForRetrieverRun.html
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b59b6b896991-0
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langchain.callbacks.tracers.schemas.TracerSessionBase¶
class langchain.callbacks.tracers.schemas.TracerSessionBase[source]¶
Bases: TracerSessionV1Base
Base class for TracerSession.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param extra: Optional[Dict[str, Any]] = None¶
param name: Optional[str] = None¶
param start_time: datetime.datetime [Optional]¶
param tenant_id: uuid.UUID [Required]¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.schemas.TracerSessionBase.html
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b59b6b896991-1
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deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.schemas.TracerSessionBase.html
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b59b6b896991-2
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classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.schemas.TracerSessionBase.html
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langchain.callbacks.tracers.stdout.ConsoleCallbackHandler¶
class langchain.callbacks.tracers.stdout.ConsoleCallbackHandler(**kwargs: Any)[source]¶
Tracer that prints to the console.
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.
name
raise_error
run_inline
Methods
__init__(**kwargs)
get_breadcrumbs(run)
get_parents(run)
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, **kwargs)
End a trace for a chain run.
on_chain_error(error, *, run_id, **kwargs)
Handle an error for a chain run.
on_chain_start(serialized, inputs, *, run_id)
Start a trace for a chain run.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, *, run_id, **kwargs)
End a trace for an LLM run.
on_llm_error(error, *, run_id, **kwargs)
Handle an error for an LLM run.
on_llm_new_token(token, *, run_id[, ...])
Run on new LLM token.
on_llm_start(serialized, prompts, *, run_id)
Start a trace for an LLM run.
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.stdout.ConsoleCallbackHandler.html
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3ee032ce82ab-1
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Start a trace for an LLM run.
on_retriever_end(documents, *, run_id, **kwargs)
Run when Retriever ends running.
on_retriever_error(error, *, run_id, **kwargs)
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_retry(retry_state, *, run_id, **kwargs)
on_text(text, *, run_id[, parent_run_id])
Run on arbitrary text.
on_tool_end(output, *, run_id, **kwargs)
End a trace for a tool run.
on_tool_error(error, *, run_id, **kwargs)
Handle an error for a tool run.
on_tool_start(serialized, input_str, *, run_id)
Start a trace for a tool run.
__init__(**kwargs: Any) → None[source]¶
get_breadcrumbs(run: Run) → str¶
get_parents(run: Run) → List[Run]¶
on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent action.
on_agent_finish(finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent end.
on_chain_end(outputs: Dict[str, Any], *, run_id: UUID, **kwargs: Any) → None¶
End a trace for a chain run.
on_chain_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for a chain run.
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.stdout.ConsoleCallbackHandler.html
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Handle an error for a chain run.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, run_type: Optional[str] = None, **kwargs: Any) → None¶
Start a trace for a chain run.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, *, run_id: UUID, **kwargs: Any) → None¶
End a trace for an LLM run.
on_llm_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for an LLM run.
on_llm_new_token(token: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None¶
Run on new LLM token. Only available when streaming is enabled.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Start a trace for an LLM run.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, **kwargs: Any) → None¶
Run when Retriever ends running.
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.stdout.ConsoleCallbackHandler.html
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3ee032ce82ab-3
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Run when Retriever ends running.
on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Run when Retriever starts running.
on_retry(retry_state: RetryCallState, *, run_id: UUID, **kwargs: Any) → None¶
on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on arbitrary text.
on_tool_end(output: str, *, run_id: UUID, **kwargs: Any) → None¶
End a trace for a tool run.
on_tool_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for a tool run.
on_tool_start(serialized: Dict[str, Any], input_str: str, *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Start a trace for a tool run.
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.stdout.ConsoleCallbackHandler.html
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langchain.callbacks.base.CallbackManagerMixin¶
class langchain.callbacks.base.CallbackManagerMixin[source]¶
Mixin for callback manager.
Methods
__init__()
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_start(serialized, prompts, *, run_id)
Run when LLM starts running.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_tool_start(serialized, input_str, *, run_id)
Run when tool starts running.
__init__()¶
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.
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.
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[source]¶
Run when LLM starts running.
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.base.CallbackManagerMixin.html
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677ffb1ce9a7-1
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Run when LLM starts running.
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[source]¶
Run when Retriever starts running.
on_tool_start(serialized: Dict[str, Any], input_str: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any[source]¶
Run when tool starts running.
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.base.CallbackManagerMixin.html
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682805027ac9-0
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langchain.callbacks.arthur_callback.ArthurCallbackHandler¶
class langchain.callbacks.arthur_callback.ArthurCallbackHandler(arthur_model: ArthurModel)[source]¶
Callback Handler that logs to Arthur platform.
Arthur helps enterprise teams optimize model operations
and performance at scale. The Arthur API tracks model
performance, explainability, and fairness across tabular,
NLP, and CV models. Our API is model- and platform-agnostic,
and continuously scales with complex and dynamic enterprise needs.
To learn more about Arthur, visit our website at
https://www.arthur.ai/ or read the Arthur docs at
https://docs.arthur.ai/
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__(arthur_model)
Initialize callback handler.
from_credentials(model_id[, arthur_url, ...])
Initialize callback handler from Arthur credentials.
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)
On chain end, do nothing.
on_chain_error(error, **kwargs)
Do nothing when LLM chain outputs an error.
on_chain_start(serialized, inputs, **kwargs)
On chain start, do nothing.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, **kwargs)
On LLM end, send data to Arthur.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.arthur_callback.ArthurCallbackHandler.html
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682805027ac9-1
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On LLM end, send data to Arthur.
on_llm_error(error, **kwargs)
Do nothing when LLM outputs an error.
on_llm_new_token(token, **kwargs)
On new token, pass.
on_llm_start(serialized, prompts, **kwargs)
On LLM start, save the input 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_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__(arthur_model: ArthurModel) → None[source]¶
Initialize callback handler.
classmethod from_credentials(model_id: str, arthur_url: Optional[str] = 'https://app.arthur.ai', arthur_login: Optional[str] = None, arthur_password: Optional[str] = None) → ArthurCallbackHandler[source]¶
Initialize callback handler from Arthur credentials.
Parameters
model_id (str) – The ID of the arthur model to log to.
arthur_url (str, optional) – The URL of the Arthur instance to log to.
Defaults to “https://app.arthur.ai”.
arthur_login (str, optional) – The login to use to connect to Arthur.
Defaults to None.
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.arthur_callback.ArthurCallbackHandler.html
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682805027ac9-2
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Defaults to None.
arthur_password (str, optional) – The password to use to connect to
Arthur. Defaults to None.
Returns
The initialized callback handler.
Return type
ArthurCallbackHandler
on_agent_action(action: AgentAction, **kwargs: Any) → Any[source]¶
Do nothing when agent takes a specific action.
on_agent_finish(finish: AgentFinish, **kwargs: Any) → None[source]¶
Do nothing
on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None[source]¶
On chain end, do nothing.
on_chain_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Do nothing when LLM chain outputs an error.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) → None[source]¶
On chain start, do nothing.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
On LLM end, send data to Arthur.
on_llm_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Do nothing when LLM outputs an error.
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
On new token, pass.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.arthur_callback.ArthurCallbackHandler.html
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682805027ac9-3
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On LLM start, save the input prompts
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever ends running.
on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
on_text(text: str, **kwargs: Any) → None[source]¶
Do nothing
on_tool_end(output: str, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any) → None[source]¶
Do nothing when tool ends.
on_tool_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Do nothing when tool outputs an error.
on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None[source]¶
Do nothing when tool starts.
Examples using ArthurCallbackHandler¶
Arthur
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.arthur_callback.ArthurCallbackHandler.html
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f0d8c83b3d70-0
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langchain.callbacks.clearml_callback.ClearMLCallbackHandler¶
class langchain.callbacks.clearml_callback.ClearMLCallbackHandler(task_type: Optional[str] = 'inference', project_name: Optional[str] = 'langchain_callback_demo', tags: Optional[Sequence] = None, task_name: Optional[str] = None, visualize: bool = False, complexity_metrics: bool = False, stream_logs: bool = False)[source]¶
Callback Handler that logs to ClearML.
Parameters
job_type (str) – The type of clearml task such as “inference”, “testing” or “qc”
project_name (str) – The clearml project name
tags (list) – Tags to add to the task
task_name (str) – Name of the clearml task
visualize (bool) – Whether to visualize the run.
complexity_metrics (bool) – Whether to log complexity metrics
stream_logs (bool) – Whether to stream callback actions to ClearML
This handler will utilize the associated callback method 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 the ClearML console.
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__([task_type, project_name, tags, ...])
Initialize callback handler.
analyze_text(text)
Analyze text using textstat and spacy.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.clearml_callback.ClearMLCallbackHandler.html
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f0d8c83b3d70-1
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analyze_text(text)
Analyze text using textstat and spacy.
flush_tracker([name, langchain_asset, finish])
Flush the tracker and setup the session.
get_custom_callback_meta()
on_agent_action(action, **kwargs)
Run on agent action.
on_agent_finish(finish, **kwargs)
Run when agent ends running.
on_chain_end(outputs, **kwargs)
Run when chain ends running.
on_chain_error(error, **kwargs)
Run when chain errors.
on_chain_start(serialized, inputs, **kwargs)
Run when chain starts running.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, **kwargs)
Run when LLM ends running.
on_llm_error(error, **kwargs)
Run when LLM errors.
on_llm_new_token(token, **kwargs)
Run when LLM generates a new token.
on_llm_start(serialized, prompts, **kwargs)
Run when LLM starts.
on_retriever_end(documents, *, run_id[, ...])
Run when Retriever ends running.
on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_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.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.clearml_callback.ClearMLCallbackHandler.html
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f0d8c83b3d70-2
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Run when tool starts running.
reset_callback_meta()
Reset the callback metadata.
__init__(task_type: Optional[str] = 'inference', project_name: Optional[str] = 'langchain_callback_demo', tags: Optional[Sequence] = None, task_name: Optional[str] = None, visualize: bool = False, complexity_metrics: bool = False, stream_logs: bool = False) → None[source]¶
Initialize callback handler.
analyze_text(text: str) → dict[source]¶
Analyze text using textstat and spacy.
Parameters
text (str) – The text to analyze.
Returns
A dictionary containing the complexity metrics.
Return type
(dict)
flush_tracker(name: Optional[str] = None, langchain_asset: Any = None, finish: bool = False) → None[source]¶
Flush the tracker and setup the session.
Everything after this will be a new table.
Parameters
name – Name of the performed session so far so it is identifiable
langchain_asset – The langchain asset to save.
finish – Whether to finish the run.
Returns – None
get_custom_callback_meta() → Dict[str, Any]¶
on_agent_action(action: AgentAction, **kwargs: Any) → Any[source]¶
Run on agent action.
on_agent_finish(finish: AgentFinish, **kwargs: Any) → None[source]¶
Run when agent ends running.
on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Run when chain ends running.
on_chain_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when chain errors.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Run when chain starts running.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.clearml_callback.ClearMLCallbackHandler.html
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f0d8c83b3d70-3
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Run when chain starts running.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
Run when LLM ends running.
on_llm_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when LLM errors.
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Run when LLM generates a new token.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
Run when LLM starts.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever ends running.
on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
on_text(text: str, **kwargs: Any) → None[source]¶
Run when agent is ending.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.clearml_callback.ClearMLCallbackHandler.html
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f0d8c83b3d70-4
|
Run when agent is ending.
on_tool_end(output: str, **kwargs: Any) → None[source]¶
Run when tool ends running.
on_tool_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when tool errors.
on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None[source]¶
Run when tool starts running.
reset_callback_meta() → None¶
Reset the callback metadata.
Examples using ClearMLCallbackHandler¶
ClearML
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.clearml_callback.ClearMLCallbackHandler.html
|
a1c895e8ad03-0
|
langchain.callbacks.infino_callback.import_infino¶
langchain.callbacks.infino_callback.import_infino() → Any[source]¶
Import the infino client.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.infino_callback.import_infino.html
|
df08fa1c0bc3-0
|
langchain.callbacks.base.RetrieverManagerMixin¶
class langchain.callbacks.base.RetrieverManagerMixin[source]¶
Mixin for Retriever callbacks.
Methods
__init__()
on_retriever_end(documents, *, run_id[, ...])
Run when Retriever ends running.
on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
__init__()¶
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any[source]¶
Run when Retriever ends running.
on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any[source]¶
Run when Retriever errors.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.base.RetrieverManagerMixin.html
|
83a3ba85f5e0-0
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langchain.callbacks.flyte_callback.FlyteCallbackHandler¶
class langchain.callbacks.flyte_callback.FlyteCallbackHandler[source]¶
This callback handler that is used within a Flyte task.
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__()
Initialize callback handler.
get_custom_callback_meta()
on_agent_action(action, **kwargs)
Run on agent action.
on_agent_finish(finish, **kwargs)
Run when agent ends running.
on_chain_end(outputs, **kwargs)
Run when chain ends running.
on_chain_error(error, **kwargs)
Run when chain errors.
on_chain_start(serialized, inputs, **kwargs)
Run when chain starts running.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, **kwargs)
Run when LLM ends running.
on_llm_error(error, **kwargs)
Run when LLM errors.
on_llm_new_token(token, **kwargs)
Run when LLM generates a new token.
on_llm_start(serialized, prompts, **kwargs)
Run when LLM starts.
on_retriever_end(documents, *, run_id[, ...])
Run when Retriever ends running.
on_retriever_error(error, *, run_id[, ...])
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.flyte_callback.FlyteCallbackHandler.html
|
83a3ba85f5e0-1
|
on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
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__() → None[source]¶
Initialize callback handler.
get_custom_callback_meta() → Dict[str, Any]¶
on_agent_action(action: AgentAction, **kwargs: Any) → Any[source]¶
Run on agent action.
on_agent_finish(finish: AgentFinish, **kwargs: Any) → None[source]¶
Run when agent ends running.
on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Run when chain ends running.
on_chain_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when chain errors.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Run when chain starts running.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.flyte_callback.FlyteCallbackHandler.html
|
83a3ba85f5e0-2
|
Run when a chat model starts running.
on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
Run when LLM ends running.
on_llm_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when LLM errors.
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Run when LLM generates a new token.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
Run when LLM starts.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever ends running.
on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
on_text(text: str, **kwargs: Any) → None[source]¶
Run when agent is ending.
on_tool_end(output: str, **kwargs: Any) → None[source]¶
Run when tool ends running.
on_tool_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when tool errors.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.flyte_callback.FlyteCallbackHandler.html
|
83a3ba85f5e0-3
|
Run when tool errors.
on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None[source]¶
Run when tool starts running.
reset_callback_meta() → None¶
Reset the callback metadata.
Examples using FlyteCallbackHandler¶
Flyte
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.flyte_callback.FlyteCallbackHandler.html
|
539b43e39b74-0
|
langchain.callbacks.tracers.evaluation.EvaluatorCallbackHandler¶
class langchain.callbacks.tracers.evaluation.EvaluatorCallbackHandler(evaluators: Sequence[RunEvaluator], max_workers: Optional[int] = None, client: Optional[Client] = None, example_id: Optional[Union[str, UUID]] = None, skip_unfinished: bool = True, project_name: Optional[str] = 'evaluators', **kwargs: Any)[source]¶
A tracer that runs a run evaluator whenever a run is persisted.
Parameters
evaluators (Sequence[RunEvaluator]) – The run evaluators to apply to all top level runs.
max_workers (int, optional) – The maximum number of worker threads to use for running the evaluators.
If not specified, it will default to the number of evaluators.
client (LangSmith Client, optional) – The LangSmith client instance to use for evaluating the runs.
If not specified, a new instance will be created.
example_id (Union[UUID, str], optional) – The example ID to be associated with the runs.
project_name (str, optional) – The LangSmith project name to be organize eval chain runs under.
example_id¶
The example ID associated with the runs.
Type
Union[UUID, None]
client¶
The LangSmith client instance used for evaluating the runs.
Type
Client
evaluators¶
The sequence of run evaluators to be executed.
Type
Sequence[RunEvaluator]
executor¶
The thread pool executor used for running the evaluators.
Type
ThreadPoolExecutor
futures¶
The set of futures representing the running evaluators.
Type
Set[Future]
skip_unfinished¶
Whether to skip runs that are not finished or raised
an error.
Type
bool
project_name¶
The LangSmith project name to be organize eval chain runs under.
Type
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.evaluation.EvaluatorCallbackHandler.html
|
539b43e39b74-1
|
project_name¶
The LangSmith project name to be organize eval chain runs under.
Type
Optional[str]
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.
name
raise_error
run_inline
Methods
__init__(evaluators[, max_workers, client, ...])
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, **kwargs)
End a trace for a chain run.
on_chain_error(error, *, run_id, **kwargs)
Handle an error for a chain run.
on_chain_start(serialized, inputs, *, run_id)
Start a trace for a chain run.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, *, run_id, **kwargs)
End a trace for an LLM run.
on_llm_error(error, *, run_id, **kwargs)
Handle an error for an LLM run.
on_llm_new_token(token, *, run_id[, ...])
Run on new LLM token.
on_llm_start(serialized, prompts, *, run_id)
Start a trace for an LLM run.
on_retriever_end(documents, *, run_id, **kwargs)
Run when Retriever ends running.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.evaluation.EvaluatorCallbackHandler.html
|
539b43e39b74-2
|
Run when Retriever ends running.
on_retriever_error(error, *, run_id, **kwargs)
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_retry(retry_state, *, run_id, **kwargs)
on_text(text, *, run_id[, parent_run_id])
Run on arbitrary text.
on_tool_end(output, *, run_id, **kwargs)
End a trace for a tool run.
on_tool_error(error, *, run_id, **kwargs)
Handle an error for a tool run.
on_tool_start(serialized, input_str, *, run_id)
Start a trace for a tool run.
wait_for_futures()
Wait for all futures to complete.
__init__(evaluators: Sequence[RunEvaluator], max_workers: Optional[int] = None, client: Optional[Client] = None, example_id: Optional[Union[str, UUID]] = None, skip_unfinished: bool = True, project_name: Optional[str] = 'evaluators', **kwargs: Any) → None[source]¶
on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent action.
on_agent_finish(finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent end.
on_chain_end(outputs: Dict[str, Any], *, run_id: UUID, **kwargs: Any) → None¶
End a trace for a chain run.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.evaluation.EvaluatorCallbackHandler.html
|
539b43e39b74-3
|
End a trace for a chain run.
on_chain_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for a chain run.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, run_type: Optional[str] = None, **kwargs: Any) → None¶
Start a trace for a chain run.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, *, run_id: UUID, **kwargs: Any) → None¶
End a trace for an LLM run.
on_llm_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for an LLM run.
on_llm_new_token(token: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None¶
Run on new LLM token. Only available when streaming is enabled.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Start a trace for an LLM run.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.evaluation.EvaluatorCallbackHandler.html
|
539b43e39b74-4
|
Start a trace for an LLM run.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, **kwargs: Any) → None¶
Run when Retriever ends running.
on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Run when Retriever starts running.
on_retry(retry_state: RetryCallState, *, run_id: UUID, **kwargs: Any) → None¶
on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on arbitrary text.
on_tool_end(output: str, *, run_id: UUID, **kwargs: Any) → None¶
End a trace for a tool run.
on_tool_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for a tool run.
on_tool_start(serialized: Dict[str, Any], input_str: str, *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Start a trace for a tool run.
wait_for_futures() → None[source]¶
Wait for all futures to complete.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.evaluation.EvaluatorCallbackHandler.html
|
48584a9c3479-0
|
langchain.callbacks.tracers.stdout.try_json_stringify¶
langchain.callbacks.tracers.stdout.try_json_stringify(obj: Any, fallback: str) → str[source]¶
Try to stringify an object to JSON.
:param obj: Object to stringify.
:param fallback: Fallback string to return if the object cannot be stringified.
Returns
A JSON string if the object can be stringified, otherwise the fallback string.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.stdout.try_json_stringify.html
|
26c31369bcc2-0
|
langchain.callbacks.manager.get_openai_callback¶
langchain.callbacks.manager.get_openai_callback() → Generator[OpenAICallbackHandler, None, None][source]¶
Get the OpenAI callback handler in a context manager.
which conveniently exposes token and cost information.
Returns
The OpenAI callback handler.
Return type
OpenAICallbackHandler
Example
>>> with get_openai_callback() as cb:
... # Use the OpenAI callback handler
Examples using get_openai_callback¶
Token counting
Tracking token usage
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.get_openai_callback.html
|
ef9fff39fa1b-0
|
langchain.callbacks.utils.import_textstat¶
langchain.callbacks.utils.import_textstat() → Any[source]¶
Import the textstat python package and raise an error if it is not installed.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.utils.import_textstat.html
|
7db02ad0c567-0
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langchain.callbacks.manager.atrace_as_chain_group¶
langchain.callbacks.manager.atrace_as_chain_group(group_name: str, callback_manager: Optional[AsyncCallbackManager] = None, *, project_name: Optional[str] = None, example_id: Optional[Union[str, UUID]] = None, tags: Optional[List[str]] = None) → AsyncGenerator[AsyncCallbackManager, None][source]¶
Get an async callback manager for a chain group in a context manager.
Useful for grouping different async 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.
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.
tags (List[str], optional) – The inheritable tags to apply to all runs.
Defaults to None.
Returns
The async callback manager for the chain group.
Return type
AsyncCallbackManager
Example
>>> async with atrace_as_chain_group("group_name") as manager:
... # Use the async callback manager for the chain group
... await llm.apredict("Foo", callbacks=manager)
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.atrace_as_chain_group.html
|
47ebe9389354-0
|
langchain.callbacks.tracers.langchain.wait_for_all_tracers¶
langchain.callbacks.tracers.langchain.wait_for_all_tracers() → None[source]¶
Wait for all tracers to finish.
Examples using wait_for_all_tracers¶
LangSmith Walkthrough
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.langchain.wait_for_all_tracers.html
|
cc5336f23b85-0
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langchain.callbacks.wandb_callback.analyze_text¶
langchain.callbacks.wandb_callback.analyze_text(text: str, complexity_metrics: bool = True, visualize: bool = True, nlp: Any = None, output_dir: Optional[Union[str, Path]] = None) → dict[source]¶
Analyze text using textstat and spacy.
Parameters
text (str) – The text to analyze.
complexity_metrics (bool) – Whether to compute complexity metrics.
visualize (bool) – Whether to visualize the text.
nlp (spacy.lang) – The spacy language model to use for visualization.
output_dir (str) – The directory to save the visualization files to.
Returns
A dictionary containing the complexity metrics and visualizationfiles serialized in a wandb.Html element.
Return type
(dict)
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.wandb_callback.analyze_text.html
|
032a03a0d5f8-0
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langchain.callbacks.tracers.schemas.RunTypeEnum¶
langchain.callbacks.tracers.schemas.RunTypeEnum() → RunTypeEnum[source]¶
RunTypeEnum.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.schemas.RunTypeEnum.html
|
02f9c595cae6-0
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langchain.callbacks.argilla_callback.ArgillaCallbackHandler¶
class langchain.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 – 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/guides/llms/practical_guides/use_argilla_callback_in_langchain.html.
workspace_name – 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 – 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 http://localhost:6900
will be used.
api_key – API Key to connect to the Argilla Server. Defaults to None, which
means that either ARGILLA_API_KEY environment variable or the default
argilla.apikey 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.llms import OpenAI
>>> from langchain.callbacks import ArgillaCallbackHandler
>>> argilla_callback = ArgillaCallbackHandler(
... dataset_name="my-dataset",
... workspace_name="my-workspace",
... api_url="http://localhost:6900",
... api_key="argilla.apikey",
... )
>>> llm = OpenAI(
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.argilla_callback.ArgillaCallbackHandler.html
|
02f9c595cae6-1
|
... )
>>> 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 – 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/guides/llms/practical_guides/use_argilla_callback_in_langchain.html.
workspace_name – 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 – 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
http://localhost:6900 will be used.
api_key – API Key to connect to the Argilla Server. Defaults to None, which
means that either ARGILLA_API_KEY environment variable or the default
argilla.apikey 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
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.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.argilla_callback.ArgillaCallbackHandler.html
|
02f9c595cae6-2
|
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[, ...])
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.argilla_callback.ArgillaCallbackHandler.html
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Run when Retriever starts running.
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 – 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/guides/llms/practical_guides/use_argilla_callback_in_langchain.html.
workspace_name – 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 – 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
http://localhost:6900 will be used.
api_key – API Key to connect to the Argilla Server. Defaults to None, which
means that either ARGILLA_API_KEY environment variable or the default
argilla.apikey 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.
on_agent_action(action: AgentAction, **kwargs: Any) → Any[source]¶
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.argilla_callback.ArgillaCallbackHandler.html
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on_agent_action(action: AgentAction, **kwargs: Any) → Any[source]¶
Do nothing when agent takes a specific action.
on_agent_finish(finish: AgentFinish, **kwargs: Any) → None[source]¶
Do nothing
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.
on_chain_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Do nothing when LLM chain outputs an error.
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.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
Log records to Argilla when an LLM ends.
on_llm_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Do nothing when LLM outputs an error.
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.argilla_callback.ArgillaCallbackHandler.html
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Do nothing when LLM outputs an error.
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Do nothing when a new token is generated.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
Save the prompts in memory when an LLM starts.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever ends running.
on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
on_text(text: str, **kwargs: Any) → None[source]¶
Do nothing
on_tool_end(output: str, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any) → None[source]¶
Do nothing when tool ends.
on_tool_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Do nothing when tool outputs an error.
on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None[source]¶
Do nothing when tool starts.
Examples using ArgillaCallbackHandler¶
Argilla
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.argilla_callback.ArgillaCallbackHandler.html
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langchain.callbacks.openai_info.get_openai_token_cost_for_model¶
langchain.callbacks.openai_info.get_openai_token_cost_for_model(model_name: str, num_tokens: int, is_completion: bool = False) → float[source]¶
Get the cost in USD for a given model and number of tokens.
Parameters
model_name – Name of the model
num_tokens – Number of tokens.
is_completion – Whether the model is used for completion or not.
Defaults to False.
Returns
Cost in USD.
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.openai_info.get_openai_token_cost_for_model.html
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langchain.callbacks.flyte_callback.analyze_text¶
langchain.callbacks.flyte_callback.analyze_text(text: str, nlp: Any = None, textstat: Any = None) → dict[source]¶
Analyze text using textstat and spacy.
Parameters
text (str) – The text to analyze.
nlp (spacy.lang) – The spacy language model to use for visualization.
Returns
A dictionary containing the complexity metrics and visualizationfiles serialized to HTML string.
Return type
(dict)
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.flyte_callback.analyze_text.html
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langchain.callbacks.streaming_stdout_final_only.FinalStreamingStdOutCallbackHandler¶
class langchain.callbacks.streaming_stdout_final_only.FinalStreamingStdOutCallbackHandler(*, answer_prefix_tokens: Optional[List[str]] = None, strip_tokens: bool = True, stream_prefix: bool = False)[source]¶
Callback handler for streaming in agents.
Only works with agents using LLMs that support streaming.
Only the final output of the agent will be streamed.
Instantiate FinalStreamingStdOutCallbackHandler.
Parameters
answer_prefix_tokens – Token sequence that prefixes the answer.
Default is [“Final”, “Answer”, “:”]
strip_tokens – Ignore white spaces and new lines when comparing
answer_prefix_tokens to last tokens? (to determine if answer has been
reached)
stream_prefix – Should answer prefix itself also be streamed?
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__(*[, answer_prefix_tokens, ...])
Instantiate FinalStreamingStdOutCallbackHandler.
append_to_last_tokens(token)
check_if_answer_reached()
on_agent_action(action, **kwargs)
Run on agent action.
on_agent_finish(finish, **kwargs)
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.
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streaming_stdout_final_only.FinalStreamingStdOutCallbackHandler.html
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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 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_text(text, **kwargs)
Run on arbitrary text.
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.
__init__(*, answer_prefix_tokens: Optional[List[str]] = None, strip_tokens: bool = True, stream_prefix: bool = False) → None[source]¶
Instantiate FinalStreamingStdOutCallbackHandler.
Parameters
answer_prefix_tokens – Token sequence that prefixes the answer.
Default is [“Final”, “Answer”, “:”]
strip_tokens – Ignore white spaces and new lines when comparing
answer_prefix_tokens to last tokens? (to determine if answer has been
reached)
stream_prefix – Should answer prefix itself also be streamed?
append_to_last_tokens(token: str) → None[source]¶
check_if_answer_reached() → bool[source]¶
on_agent_action(action: AgentAction, **kwargs: Any) → Any¶
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streaming_stdout_final_only.FinalStreamingStdOutCallbackHandler.html
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on_agent_action(action: AgentAction, **kwargs: Any) → Any¶
Run on agent action.
on_agent_finish(finish: AgentFinish, **kwargs: Any) → None¶
Run on agent end.
on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None¶
Run when chain ends running.
on_chain_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None¶
Run when chain errors.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) → None¶
Run when chain starts running.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, **kwargs: Any) → None¶
Run when LLM ends running.
on_llm_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None¶
Run when LLM errors.
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Run on new LLM token. Only available when streaming is enabled.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
Run when LLM starts running.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever ends running.
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streaming_stdout_final_only.FinalStreamingStdOutCallbackHandler.html
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Run when Retriever ends running.
on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
on_text(text: str, **kwargs: Any) → None¶
Run on arbitrary text.
on_tool_end(output: str, **kwargs: Any) → None¶
Run when tool ends running.
on_tool_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None¶
Run when tool errors.
on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None¶
Run when tool starts running.
Examples using FinalStreamingStdOutCallbackHandler¶
Streaming final agent output
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streaming_stdout_final_only.FinalStreamingStdOutCallbackHandler.html
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langchain.callbacks.tracers.wandb.WandbTracer¶
class langchain.callbacks.tracers.wandb.WandbTracer(run_args: Optional[WandbRunArgs] = None, **kwargs: Any)[source]¶
Callback Handler that logs to Weights and Biases.
This handler will log the model architecture and run traces to Weights and Biases.
This will ensure that all LangChain activity is logged to W&B.
Initializes the WandbTracer.
Parameters
run_args – (dict, optional) Arguments to pass to wandb.init(). If not
provided, wandb.init() will be called with no arguments. Please
refer to the wandb.init for more details.
To use W&B to monitor all LangChain activity, add this tracer like any other
LangChain callback:
```
from wandb.integration.langchain import WandbTracer
tracer = WandbTracer()
chain = LLMChain(llm, callbacks=[tracer])
# …end of notebook / script:
tracer.finish()
```
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__([run_args])
Initializes the WandbTracer.
finish()
Waits for all asynchronous processes to finish and data to upload.
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, **kwargs)
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on_chain_end(outputs, *, run_id, **kwargs)
End a trace for a chain run.
on_chain_error(error, *, run_id, **kwargs)
Handle an error for a chain run.
on_chain_start(serialized, inputs, *, run_id)
Start a trace for a chain run.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, *, run_id, **kwargs)
End a trace for an LLM run.
on_llm_error(error, *, run_id, **kwargs)
Handle an error for an LLM run.
on_llm_new_token(token, *, run_id[, ...])
Run on new LLM token.
on_llm_start(serialized, prompts, *, run_id)
Start a trace for an LLM run.
on_retriever_end(documents, *, run_id, **kwargs)
Run when Retriever ends running.
on_retriever_error(error, *, run_id, **kwargs)
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_retry(retry_state, *, run_id, **kwargs)
on_text(text, *, run_id[, parent_run_id])
Run on arbitrary text.
on_tool_end(output, *, run_id, **kwargs)
End a trace for a tool run.
on_tool_error(error, *, run_id, **kwargs)
Handle an error for a tool run.
on_tool_start(serialized, input_str, *, run_id)
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.wandb.WandbTracer.html
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on_tool_start(serialized, input_str, *, run_id)
Start a trace for a tool run.
__init__(run_args: Optional[WandbRunArgs] = None, **kwargs: Any) → None[source]¶
Initializes the WandbTracer.
Parameters
run_args – (dict, optional) Arguments to pass to wandb.init(). If not
provided, wandb.init() will be called with no arguments. Please
refer to the wandb.init for more details.
To use W&B to monitor all LangChain activity, add this tracer like any other
LangChain callback:
```
from wandb.integration.langchain import WandbTracer
tracer = WandbTracer()
chain = LLMChain(llm, callbacks=[tracer])
# …end of notebook / script:
tracer.finish()
```
finish() → None[source]¶
Waits for all asynchronous processes to finish and data to upload.
Proxy for wandb.finish().
on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent action.
on_agent_finish(finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent end.
on_chain_end(outputs: Dict[str, Any], *, run_id: UUID, **kwargs: Any) → None¶
End a trace for a chain run.
on_chain_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for a chain run.
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.wandb.WandbTracer.html
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Handle an error for a chain run.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, run_type: Optional[str] = None, **kwargs: Any) → None¶
Start a trace for a chain run.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, *, run_id: UUID, **kwargs: Any) → None¶
End a trace for an LLM run.
on_llm_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for an LLM run.
on_llm_new_token(token: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None¶
Run on new LLM token. Only available when streaming is enabled.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Start a trace for an LLM run.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, **kwargs: Any) → None¶
Run when Retriever ends running.
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.wandb.WandbTracer.html
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Run when Retriever ends running.
on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Run when Retriever starts running.
on_retry(retry_state: RetryCallState, *, run_id: UUID, **kwargs: Any) → None¶
on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on arbitrary text.
on_tool_end(output: str, *, run_id: UUID, **kwargs: Any) → None¶
End a trace for a tool run.
on_tool_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for a tool run.
on_tool_start(serialized: Dict[str, Any], input_str: str, *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Start a trace for a tool run.
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https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.wandb.WandbTracer.html
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langchain.callbacks.manager.RunManager¶
class langchain.callbacks.manager.RunManager(*, 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]¶
Sync Run Manager.
Initialize the run manager.
Parameters
run_id (UUID) – The ID of the run.
handlers (List[BaseCallbackHandler]) – The list of handlers.
inheritable_handlers (List[BaseCallbackHandler]) – The list of inheritable handlers.
parent_run_id (UUID, optional) – The ID of the parent run.
Defaults to None.
tags (Optional[List[str]]) – The list of tags.
inheritable_tags (Optional[List[str]]) – The list of inheritable tags.
metadata (Optional[Dict[str, Any]]) – The metadata.
inheritable_metadata (Optional[Dict[str, Any]]) – The inheritable metadata.
Methods
__init__(*, run_id, handlers, ...[, ...])
Initialize the run manager.
get_noop_manager()
Return a manager that doesn't perform any operations.
on_retry(retry_state, **kwargs)
on_text(text, **kwargs)
Run when text is received.
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.RunManager.html
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on_text(text, **kwargs)
Run when text is received.
__init__(*, run_id: UUID, handlers: List[BaseCallbackHandler], inheritable_handlers: List[BaseCallbackHandler], parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, inheritable_tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, inheritable_metadata: Optional[Dict[str, Any]] = None) → None¶
Initialize the run manager.
Parameters
run_id (UUID) – The ID of the run.
handlers (List[BaseCallbackHandler]) – The list of handlers.
inheritable_handlers (List[BaseCallbackHandler]) – The list of inheritable handlers.
parent_run_id (UUID, optional) – The ID of the parent run.
Defaults to None.
tags (Optional[List[str]]) – The list of tags.
inheritable_tags (Optional[List[str]]) – The list of inheritable tags.
metadata (Optional[Dict[str, Any]]) – The metadata.
inheritable_metadata (Optional[Dict[str, Any]]) – The inheritable metadata.
classmethod get_noop_manager() → BRM¶
Return a manager that doesn’t perform any operations.
Returns
The noop manager.
Return type
BaseRunManager
on_retry(retry_state: RetryCallState, **kwargs: Any) → None[source]¶
on_text(text: str, **kwargs: Any) → Any[source]¶
Run when text is received.
Parameters
text (str) – The received text.
Returns
The result of the callback.
Return type
Any
|
https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.RunManager.html
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langchain.callbacks.manager.AsyncCallbackManager¶
class langchain.callbacks.manager.AsyncCallbackManager(handlers: List[BaseCallbackHandler], inheritable_handlers: Optional[List[BaseCallbackHandler]] = None, 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 that handles callbacks from LangChain.
Initialize callback manager.
Attributes
is_async
Return whether the handler is async.
Methods
__init__(handlers[, inheritable_handlers, ...])
Initialize callback manager.
add_handler(handler[, inherit])
Add a handler to the callback manager.
add_metadata(metadata[, inherit])
add_tags(tags[, inherit])
configure([inheritable_callbacks, ...])
Configure the async callback manager.
on_chain_start(serialized, inputs[, run_id])
Run when chain starts running.
on_chat_model_start(serialized, messages, ...)
Run when LLM starts running.
on_llm_start(serialized, prompts, **kwargs)
Run when LLM starts running.
on_retriever_start(serialized, query[, ...])
Run when retriever starts running.
on_tool_start(serialized, input_str[, ...])
Run when tool starts running.
remove_handler(handler)
Remove a handler from the callback manager.
remove_metadata(keys)
remove_tags(tags)
set_handler(handler[, inherit])
Set handler as the only handler on the callback manager.
set_handlers(handlers[, inherit])
Set handlers as the only handlers on the callback manager.
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Set handlers as the only handlers on the callback manager.
__init__(handlers: List[BaseCallbackHandler], inheritable_handlers: Optional[List[BaseCallbackHandler]] = None, 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 callback manager.
add_handler(handler: BaseCallbackHandler, inherit: bool = True) → None¶
Add a handler to the callback manager.
add_metadata(metadata: Dict[str, Any], inherit: bool = True) → None¶
add_tags(tags: List[str], inherit: bool = True) → None¶
classmethod configure(inheritable_callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, local_callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, verbose: bool = False, inheritable_tags: Optional[List[str]] = None, local_tags: Optional[List[str]] = None, inheritable_metadata: Optional[Dict[str, Any]] = None, local_metadata: Optional[Dict[str, Any]] = None) → AsyncCallbackManager[source]¶
Configure the async callback manager.
Parameters
inheritable_callbacks (Optional[Callbacks], optional) – The inheritable
callbacks. Defaults to None.
local_callbacks (Optional[Callbacks], optional) – The local callbacks.
Defaults to None.
verbose (bool, optional) – Whether to enable verbose mode. Defaults to False.
inheritable_tags (Optional[List[str]], optional) – The inheritable tags.
Defaults to None.
local_tags (Optional[List[str]], optional) – The local tags.
Defaults to None.
inheritable_metadata (Optional[Dict[str, Any]], optional) – The inheritable
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inheritable_metadata (Optional[Dict[str, Any]], optional) – The inheritable
metadata. Defaults to None.
local_metadata (Optional[Dict[str, Any]], optional) – The local metadata.
Defaults to None.
Returns
The configured async callback manager.
Return type
AsyncCallbackManager
async on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], run_id: Optional[UUID] = None, **kwargs: Any) → AsyncCallbackManagerForChainRun[source]¶
Run when chain starts running.
Parameters
serialized (Dict[str, Any]) – The serialized chain.
inputs (Dict[str, Any]) – The inputs to the chain.
run_id (UUID, optional) – The ID of the run. Defaults to None.
Returns
The async callback managerfor the chain run.
Return type
AsyncCallbackManagerForChainRun
async on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], **kwargs: Any) → List[AsyncCallbackManagerForLLMRun][source]¶
Run when LLM starts running.
Parameters
serialized (Dict[str, Any]) – The serialized LLM.
messages (List[List[BaseMessage]]) – The list of messages.
run_id (UUID, optional) – The ID of the run. Defaults to None.
Returns
The list ofasync callback managers, one for each LLM Run
corresponding to each inner message list.
Return type
List[AsyncCallbackManagerForLLMRun]
async on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → List[AsyncCallbackManagerForLLMRun][source]¶
Run when LLM starts running.
Parameters
serialized (Dict[str, Any]) – The serialized LLM.
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Parameters
serialized (Dict[str, Any]) – The serialized LLM.
prompts (List[str]) – The list of prompts.
run_id (UUID, optional) – The ID of the run. Defaults to None.
Returns
The list of asynccallback managers, one for each LLM Run corresponding
to each prompt.
Return type
List[AsyncCallbackManagerForLLMRun]
async on_retriever_start(serialized: Dict[str, Any], query: str, run_id: Optional[UUID] = None, parent_run_id: Optional[UUID] = None, **kwargs: Any) → AsyncCallbackManagerForRetrieverRun[source]¶
Run when retriever starts running.
async on_tool_start(serialized: Dict[str, Any], input_str: str, run_id: Optional[UUID] = None, parent_run_id: Optional[UUID] = None, **kwargs: Any) → AsyncCallbackManagerForToolRun[source]¶
Run when tool starts running.
Parameters
serialized (Dict[str, Any]) – The serialized tool.
input_str (str) – The input to the tool.
run_id (UUID, optional) – The ID of the run. Defaults to None.
parent_run_id (UUID, optional) – The ID of the parent run.
Defaults to None.
Returns
The async callback managerfor the tool run.
Return type
AsyncCallbackManagerForToolRun
remove_handler(handler: BaseCallbackHandler) → None¶
Remove a handler from the callback manager.
remove_metadata(keys: List[str]) → None¶
remove_tags(tags: List[str]) → None¶
set_handler(handler: BaseCallbackHandler, inherit: bool = True) → None¶
Set handler as the only handler on the callback manager.
set_handlers(handlers: List[BaseCallbackHandler], inherit: bool = True) → None¶
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Set handlers as the only handlers on the callback manager.
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langchain.callbacks.comet_ml_callback.import_comet_ml¶
langchain.callbacks.comet_ml_callback.import_comet_ml() → Any[source]¶
Import comet_ml and raise an error if it is not installed.
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langchain.callbacks.context_callback.import_context¶
langchain.callbacks.context_callback.import_context() → Any[source]¶
Import the getcontext package.
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langchain.callbacks.streamlit.streamlit_callback_handler.LLMThoughtLabeler¶
class langchain.callbacks.streamlit.streamlit_callback_handler.LLMThoughtLabeler[source]¶
Generates markdown labels for LLMThought containers. Pass a custom
subclass of this to StreamlitCallbackHandler to override its default
labeling logic.
Methods
__init__()
get_final_agent_thought_label()
Return the markdown label for the agent's final thought - the "Now I have the answer" thought, that doesn't involve a tool.
get_history_label()
Return a markdown label for the special 'history' container that contains overflow thoughts.
get_initial_label()
Return the markdown label for a new LLMThought that doesn't have an associated tool yet.
get_tool_label(tool, is_complete)
Return the label for an LLMThought that has an associated tool.
__init__()¶
get_final_agent_thought_label() → str[source]¶
Return the markdown label for the agent’s final thought -
the “Now I have the answer” thought, that doesn’t involve
a tool.
get_history_label() → str[source]¶
Return a markdown label for the special ‘history’ container
that contains overflow thoughts.
get_initial_label() → str[source]¶
Return the markdown label for a new LLMThought that doesn’t have
an associated tool yet.
get_tool_label(tool: ToolRecord, is_complete: bool) → str[source]¶
Return the label for an LLMThought that has an associated
tool.
Parameters
tool – The tool’s ToolRecord
is_complete – True if the thought is complete; False if the thought
is still receiving input.
Return type
The markdown label for the thought’s container.
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langchain.callbacks.manager.AsyncCallbackManagerForToolRun¶
class langchain.callbacks.manager.AsyncCallbackManagerForToolRun(*, 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 tool 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_child([tag])
Get a child callback manager.
get_noop_manager()
Return a manager that doesn't perform any operations.
on_retry(retry_state, **kwargs)
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.
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on_tool_error(error, **kwargs)
Run when tool errors.
__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.
get_child(tag: Optional[str] = None) → AsyncCallbackManager¶
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
AsyncCallbackManager
classmethod get_noop_manager() → BRM¶
Return a manager that doesn’t perform any operations.
Returns
The noop manager.
Return type
BaseRunManager
async on_retry(retry_state: RetryCallState, **kwargs: Any) → None¶
async on_text(text: str, **kwargs: Any) → Any¶
Run when text is received.
Parameters
text (str) – The received text.
Returns
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Parameters
text (str) – The received text.
Returns
The result of the callback.
Return type
Any
async on_tool_end(output: str, **kwargs: Any) → None[source]¶
Run when tool ends running.
Parameters
output (str) – The output of the tool.
async on_tool_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when tool errors.
Parameters
error (Exception or KeyboardInterrupt) – The error.
Examples using AsyncCallbackManagerForToolRun¶
Defining Custom Tools
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langchain.callbacks.mlflow_callback.construct_html_from_prompt_and_generation¶
langchain.callbacks.mlflow_callback.construct_html_from_prompt_and_generation(prompt: str, generation: str) → Any[source]¶
Construct an html element from a prompt and a generation.
Parameters
prompt (str) – The prompt.
generation (str) – The generation.
Returns
The html string.
Return type
(str)
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langchain.callbacks.tracers.stdout.elapsed¶
langchain.callbacks.tracers.stdout.elapsed(run: Any) → str[source]¶
Get the elapsed time of a run.
Parameters
run – any object with a start_time and end_time attribute.
Returns
A string with the elapsed time in seconds ormilliseconds if time is less than a second.
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langchain.callbacks.wandb_callback.load_json_to_dict¶
langchain.callbacks.wandb_callback.load_json_to_dict(json_path: Union[str, Path]) → dict[source]¶
Load json file to a dictionary.
Parameters
json_path (str) – The path to the json file.
Returns
The dictionary representation of the json file.
Return type
(dict)
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langchain.callbacks.clearml_callback.import_clearml¶
langchain.callbacks.clearml_callback.import_clearml() → Any[source]¶
Import the clearml python package and raise an error if it is not installed.
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langchain.callbacks.manager.CallbackManagerForRetrieverRun¶
class langchain.callbacks.manager.CallbackManagerForRetrieverRun(*, run_id: UUID, handlers: List[BaseCallbackHandler], inheritable_handlers: List[BaseCallbackHandler], parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, inheritable_tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, inheritable_metadata: Optional[Dict[str, Any]] = None)[source]¶
Callback manager for retriever 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_child([tag])
Get a child callback manager.
get_noop_manager()
Return a manager that doesn't perform any operations.
on_retriever_end(documents, **kwargs)
Run when retriever ends running.
on_retriever_error(error, **kwargs)
Run when retriever errors.
on_retry(retry_state, **kwargs)
on_text(text, **kwargs)
Run when text is received.
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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.
get_child(tag: Optional[str] = None) → CallbackManager¶
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_retriever_end(documents: Sequence[Document], **kwargs: Any) → None[source]¶
Run when retriever ends running.
on_retriever_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when retriever errors.
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Run when retriever errors.
on_retry(retry_state: RetryCallState, **kwargs: Any) → None¶
on_text(text: str, **kwargs: Any) → Any¶
Run when text is received.
Parameters
text (str) – The received text.
Returns
The result of the callback.
Return type
Any
Examples using CallbackManagerForRetrieverRun¶
Retrieve as you generate with FLARE
FLARE
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langchain.callbacks.streamlit.mutable_expander.MutableExpander¶
class langchain.callbacks.streamlit.mutable_expander.MutableExpander(parent_container: DeltaGenerator, label: str, expanded: bool)[source]¶
A Streamlit expander that can be renamed and dynamically expanded/collapsed.
Create a new MutableExpander.
Parameters
parent_container – 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 – The expander’s initial label.
expanded – The expander’s initial expanded value.
Attributes
expanded
True if the expander was created with expanded=True.
label
The 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 – 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.
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parent_container to ensure it recreates this underlying expander in the
same location onscreen.
label – The expander’s initial label.
expanded – The expander’s initial expanded value.
append_copy(other: MutableExpander) → None[source]¶
Append a copy of another MutableExpander’s children to this
MutableExpander.
clear() → None[source]¶
Remove the container and its contents entirely. A cleared container can’t
be reused.
exception(exception: BaseException, *, index: Optional[int] = None) → int[source]¶
Add an Exception element to the container and return its index.
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.
update(*, new_label: Optional[str] = None, new_expanded: Optional[bool] = None) → None[source]¶
Change the expander’s label and expanded state
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langchain.utilities.tensorflow_datasets.TensorflowDatasets¶
class langchain.utilities.tensorflow_datasets.TensorflowDatasets[source]¶
Bases: BaseModel
Access to the TensorFlow Datasets.
The Current implementation can work only with datasets that fit in a memory.
TensorFlow Datasets is a collection of datasets ready to use, with TensorFlow
or other Python ML frameworks, such as Jax. All datasets are exposed
as tf.data.Datasets.
To get started see the Guide: https://www.tensorflow.org/datasets/overview and
the list of datasets: https://www.tensorflow.org/datasets/catalog/
overview#all_datasets
You have to provide the sample_to_document_function: a function thata sample from the dataset-specific format to the Document.
dataset_name¶
the name of the dataset to load
split_name¶
the name of the split to load. Defaults to “train”.
load_max_docs¶
a limit to the number of loaded documents. Defaults to 100.
sample_to_document_function¶
a function that converts a dataset sample
to a Document
Example
from langchain.utilities import TensorflowDatasets
def mlqaen_example_to_document(example: dict) -> Document:
return Document(
page_content=decode_to_str(example["context"]),
metadata={
"id": decode_to_str(example["id"]),
"title": decode_to_str(example["title"]),
"question": decode_to_str(example["question"]),
"answer": decode_to_str(example["answers"]["text"][0]),
},
)
tsds_client = TensorflowDatasets(
dataset_name="mlqa/en",
split_name="train",
load_max_docs=MAX_DOCS,
sample_to_document_function=mlqaen_example_to_document,
)
Create a new model by parsing and validating input data from keyword arguments.
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)
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param dataset_name: str = ''¶
param load_max_docs: int = 100¶
param sample_to_document_function: Optional[Callable[[Dict], langchain.schema.document.Document]] = None¶
param split_name: str = 'train'¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
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deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
lazy_load() → Iterator[Document][source]¶
Download a selected dataset lazily.
Returns: an iterator of Documents.
load() → List[Document][source]¶
Download a selected dataset.
Returns: a list of Documents.
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
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classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny¶
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode¶
classmethod update_forward_refs(**localns: Any) → None¶
Try to update ForwardRefs on fields based on this Model, globalns and localns.
classmethod validate(value: Any) → Model¶
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.tensorflow_datasets.TensorflowDatasets.html
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b3ca74d2fb4d-0
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langchain.utilities.python.PythonREPL¶
class langchain.utilities.python.PythonREPL[source]¶
Bases: BaseModel
Simulates a standalone Python REPL.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param globals: Optional[Dict] [Optional] (alias '_globals')¶
param locals: Optional[Dict] [Optional] (alias '_locals')¶
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model¶
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model¶
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.python.PythonREPL.html
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b3ca74d2fb4d-1
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deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny¶
Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
classmethod from_orm(obj: Any) → Model¶
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode¶
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
run(command: str, timeout: Optional[int] = None) → str[source]¶
|
https://api.python.langchain.com/en/latest/utilities/langchain.utilities.python.PythonREPL.html
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