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1f9591eab472-0 | 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)
Run on a retry event.
on_text(text, **kwargs)
Run when text is received. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.AsyncCallbackManagerForRetrieverRun.html |
1f9591eab472-1 | 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: BaseException, **kwargs: Any) → None[source]¶
Run when retriever errors. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.AsyncCallbackManagerForRetrieverRun.html |
1f9591eab472-2 | Run when retriever errors.
async on_retry(retry_state: RetryCallState, **kwargs: Any) → None¶
Run on a retry event.
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 | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.AsyncCallbackManagerForRetrieverRun.html |
e350f288fc08-0 | langchain.callbacks.file.FileCallbackHandler¶
class langchain.callbacks.file.FileCallbackHandler(filename: str, mode: str = 'a', color: Optional[str] = None)[source]¶
Callback Handler that writes to a file.
Initialize callback handler.
Attributes
ignore_agent
Whether to ignore agent callbacks.
ignore_chain
Whether to ignore chain callbacks.
ignore_chat_model
Whether to ignore chat model callbacks.
ignore_llm
Whether to ignore LLM callbacks.
ignore_retriever
Whether to ignore retriever callbacks.
ignore_retry
Whether to ignore retry callbacks.
raise_error
run_inline
Methods
__init__(filename[, mode, color])
Initialize callback handler.
on_agent_action(action[, color])
Run on agent action.
on_agent_finish(finish[, color])
Run on agent end.
on_chain_end(outputs, **kwargs)
Print out that we finished a chain.
on_chain_error(error, *, run_id[, parent_run_id])
Run when chain errors.
on_chain_start(serialized, inputs, **kwargs)
Print out that we are entering a chain.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, *, run_id[, parent_run_id])
Run when LLM ends running.
on_llm_error(error, *, run_id[, parent_run_id])
Run when LLM errors.
on_llm_new_token(token, *[, chunk, ...])
Run on new LLM token.
on_llm_start(serialized, prompts, *, run_id)
Run when LLM starts running.
on_retriever_end(documents, *, run_id[, ...])
Run when Retriever ends running. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.file.FileCallbackHandler.html |
e350f288fc08-1 | Run when Retriever ends running.
on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_retry(retry_state, *, run_id[, parent_run_id])
Run on a retry event.
on_text(text[, color, end])
Run when agent ends.
on_tool_end(output[, color, ...])
If not the final action, print out observation.
on_tool_error(error, *, run_id[, parent_run_id])
Run when tool errors.
on_tool_start(serialized, input_str, *, run_id)
Run when tool starts running.
__init__(filename: str, mode: str = 'a', color: Optional[str] = None) → None[source]¶
Initialize callback handler.
on_agent_action(action: AgentAction, color: Optional[str] = None, **kwargs: Any) → Any[source]¶
Run on agent action.
on_agent_finish(finish: AgentFinish, color: Optional[str] = None, **kwargs: Any) → None[source]¶
Run on agent end.
on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Print out that we finished a chain.
on_chain_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when chain errors.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Print out that we are entering a chain. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.file.FileCallbackHandler.html |
e350f288fc08-2 | Print out that we are entering a chain.
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, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when LLM ends running.
on_llm_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when LLM errors.
on_llm_new_token(token: str, *, chunk: Optional[Union[GenerationChunk, ChatGenerationChunk]] = None, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on new LLM token. Only available when streaming is enabled.
Parameters
token (str) – The new token.
chunk (GenerationChunk | ChatGenerationChunk) – The new generated chunk,
information. (containing content and other) –
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.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever ends running. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.file.FileCallbackHandler.html |
e350f288fc08-3 | Run when Retriever ends running.
on_retriever_error(error: BaseException, *, 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_retry(retry_state: RetryCallState, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on a retry event.
on_text(text: str, color: Optional[str] = None, end: str = '', **kwargs: Any) → None[source]¶
Run when agent ends.
on_tool_end(output: str, color: Optional[str] = None, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any) → None[source]¶
If not the final action, print out observation.
on_tool_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when tool errors.
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.
Examples using FileCallbackHandler¶
Logging to file | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.file.FileCallbackHandler.html |
c73e90b53d06-0 | 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 |
c73e90b53d06-1 | 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_retry(retry_state, *, run_id[, parent_run_id])
Run on a retry event.
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. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.clearml_callback.ClearMLCallbackHandler.html |
c73e90b53d06-2 | 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__(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: BaseException, **kwargs: Any) → None[source]¶
Run when chain errors. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.clearml_callback.ClearMLCallbackHandler.html |
c73e90b53d06-3 | 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: BaseException, **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: BaseException, *, 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¶ | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.clearml_callback.ClearMLCallbackHandler.html |
c73e90b53d06-4 | Run when Retriever starts running.
on_retry(retry_state: RetryCallState, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on a retry event.
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: BaseException, **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 |
efb905858a94-0 | langchain.callbacks.tracers.langchain.get_client¶
langchain.callbacks.tracers.langchain.get_client() → Client[source]¶
Get the client. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.langchain.get_client.html |
9fb4fe829e6c-0 | langchain.callbacks.manager.ParentRunManager¶
class langchain.callbacks.manager.ParentRunManager(*, 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 Parent 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_child([tag])
Get a child callback manager.
get_noop_manager()
Return a manager that doesn't perform any operations.
on_retry(retry_state, **kwargs)
Run on a retry event.
on_text(text, **kwargs)
Run when text is received. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.ParentRunManager.html |
9fb4fe829e6c-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) → CallbackManager[source]¶
Get a child callback manager.
Parameters
tag (str, optional) – The tag for the child callback manager.
Defaults to None.
Returns
The child callback manager.
Return type
CallbackManager
classmethod get_noop_manager() → BRM¶
Return a manager that doesn’t perform any operations.
Returns
The noop manager.
Return type
BaseRunManager
on_retry(retry_state: RetryCallState, **kwargs: Any) → None¶
Run on a retry event.
on_text(text: str, **kwargs: Any) → Any¶
Run when text is received.
Parameters
text (str) – The received text. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.ParentRunManager.html |
9fb4fe829e6c-2 | 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.ParentRunManager.html |
2df61bc4c4ec-0 | langchain.callbacks.streaming_aiter_final_only.AsyncFinalIteratorCallbackHandler¶
class langchain.callbacks.streaming_aiter_final_only.AsyncFinalIteratorCallbackHandler(*, answer_prefix_tokens: Optional[List[str]] = None, strip_tokens: bool = True, stream_prefix: bool = False)[source]¶
Callback handler that returns an async iterator.
Only the final output of the agent will be iterated.
Instantiate AsyncFinalIteratorCallbackHandler.
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
always_verbose
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 AsyncFinalIteratorCallbackHandler.
aiter()
append_to_last_tokens(token)
check_if_answer_reached()
on_agent_action(action, *, run_id[, ...])
Run on agent action.
on_agent_finish(finish, *, run_id[, ...])
Run on agent end.
on_chain_end(outputs, *, run_id[, ...])
Run when chain ends running.
on_chain_error(error, *, run_id[, ...])
Run when chain errors.
on_chain_start(serialized, inputs, *, run_id)
Run when chain starts running. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streaming_aiter_final_only.AsyncFinalIteratorCallbackHandler.html |
2df61bc4c4ec-1 | 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 on new LLM token.
on_llm_start(serialized, prompts, **kwargs)
Run when LLM starts running.
on_retriever_end(documents, *, run_id[, ...])
Run on retriever end.
on_retriever_error(error, *, run_id[, ...])
Run on retriever error.
on_retriever_start(serialized, query, *, run_id)
Run on retriever start.
on_retry(retry_state, *, run_id[, parent_run_id])
Run on a retry event.
on_text(text, *, run_id[, parent_run_id, tags])
Run on arbitrary text.
on_tool_end(output, *, run_id[, ...])
Run when tool ends running.
on_tool_error(error, *, run_id[, ...])
Run when tool errors.
on_tool_start(serialized, input_str, *, run_id)
Run when tool starts running.
__init__(*, answer_prefix_tokens: Optional[List[str]] = None, strip_tokens: bool = True, stream_prefix: bool = False) → None[source]¶
Instantiate AsyncFinalIteratorCallbackHandler.
Parameters
answer_prefix_tokens – Token sequence that prefixes the answer.
Default is [“Final”, “Answer”, “:”]
strip_tokens – Ignore white spaces and new lines when comparing | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streaming_aiter_final_only.AsyncFinalIteratorCallbackHandler.html |
2df61bc4c4ec-2 | 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?
async aiter() → AsyncIterator[str]¶
append_to_last_tokens(token: str) → None[source]¶
check_if_answer_reached() → bool[source]¶
async on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None¶
Run on agent action.
async on_agent_finish(finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None¶
Run on agent end.
async on_chain_end(outputs: Dict[str, Any], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None¶
Run when chain ends running.
async on_chain_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None¶
Run when chain errors.
async on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Run when chain starts running. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streaming_aiter_final_only.AsyncFinalIteratorCallbackHandler.html |
2df61bc4c4ec-3 | Run when chain starts running.
async on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
async on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
Run when LLM ends running.
async on_llm_error(error: BaseException, **kwargs: Any) → None¶
Run when LLM errors.
async on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Run on new LLM token. Only available when streaming is enabled.
async on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
Run when LLM starts running.
async on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None¶
Run on retriever end.
async on_retriever_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None¶
Run on retriever error.
async on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Run on retriever start. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streaming_aiter_final_only.AsyncFinalIteratorCallbackHandler.html |
2df61bc4c4ec-4 | Run on retriever start.
async on_retry(retry_state: RetryCallState, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on a retry event.
async on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None¶
Run on arbitrary text.
async on_tool_end(output: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None¶
Run when tool ends running.
async on_tool_error(error: BaseException, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None¶
Run when tool errors.
async on_tool_start(serialized: Dict[str, Any], input_str: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Run when tool starts running. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.streaming_aiter_final_only.AsyncFinalIteratorCallbackHandler.html |
3331a608af0f-0 | langchain.callbacks.tracers.wandb.RunProcessor¶
class langchain.callbacks.tracers.wandb.RunProcessor(wandb_module: Any, trace_module: Any)[source]¶
Handles the conversion of a LangChain Runs into a WBTraceTree.
Methods
__init__(wandb_module, trace_module)
build_tree(runs)
Builds a nested dictionary from a list of runs. :param runs: The list of runs to build the tree from. :return: The nested dictionary representing the langchain Run in a tree structure compatible with WBTraceTree.
flatten_run(run)
Utility to flatten a nest run object into a list of runs.
modify_serialized_iterative(runs[, ...])
Utility to modify the serialized field of a list of runs dictionaries.
process_model(run)
Utility to process a run for wandb model_dict serialization.
process_span(run)
Converts a LangChain Run into a W&B Trace Span.
truncate_run_iterative(runs[, keep_keys])
Utility to truncate a list of runs dictionaries to only keep the specified
__init__(wandb_module: Any, trace_module: Any)[source]¶
build_tree(runs: List[Dict[str, Any]]) → Dict[str, Any][source]¶
Builds a nested dictionary from a list of runs.
:param runs: The list of runs to build the tree from.
:return: The nested dictionary representing the langchain Run in a tree
structure compatible with WBTraceTree.
flatten_run(run: Dict[str, Any]) → List[Dict[str, Any]][source]¶
Utility to flatten a nest run object into a list of runs.
:param run: The base run to flatten.
:return: The flattened list of runs. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.wandb.RunProcessor.html |
3331a608af0f-1 | :param run: The base run to flatten.
:return: The flattened list of runs.
modify_serialized_iterative(runs: List[Dict[str, Any]], exact_keys: Tuple[str, ...] = (), partial_keys: Tuple[str, ...] = ()) → List[Dict[str, Any]][source]¶
Utility to modify the serialized field of a list of runs dictionaries.
removes any keys that match the exact_keys and any keys that contain any of the
partial_keys.
recursively moves the dictionaries under the kwargs key to the top level.
changes the “id” field to a string “_kind” field that tells WBTraceTree how to
visualize the run. promotes the “serialized” field to the top level.
Parameters
runs – The list of runs to modify.
exact_keys – A tuple of keys to remove from the serialized field.
partial_keys – A tuple of partial keys to remove from the serialized
field.
Returns
The modified list of runs.
process_model(run: Run) → Optional[Dict[str, Any]][source]¶
Utility to process a run for wandb model_dict serialization.
:param run: The run to process.
:return: The convert model_dict to pass to WBTraceTree.
process_span(run: Run) → Optional['Span'][source]¶
Converts a LangChain Run into a W&B Trace Span.
:param run: The LangChain Run to convert.
:return: The converted W&B Trace Span.
truncate_run_iterative(runs: List[Dict[str, Any]], keep_keys: Tuple[str, ...] = ()) → List[Dict[str, Any]][source]¶
Utility to truncate a list of runs dictionaries to only keep the specifiedkeys in each run.
Parameters
runs – The list of runs to truncate.
keep_keys – The keys to keep in each run.
Returns | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.wandb.RunProcessor.html |
3331a608af0f-2 | keep_keys – The keys to keep in each run.
Returns
The truncated list of runs. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.tracers.wandb.RunProcessor.html |
f3f9058e6b3b-0 | langchain.callbacks.utils.flatten_dict¶
langchain.callbacks.utils.flatten_dict(nested_dict: Dict[str, Any], parent_key: str = '', sep: str = '_') → Dict[str, Any][source]¶
Flattens a nested dictionary into a flat dictionary.
Parameters
nested_dict (dict) – The nested dictionary to flatten.
parent_key (str) – The prefix to prepend to the keys of the flattened dict.
sep (str) – The separator to use between the parent key and the key of the
flattened dictionary.
Returns
A flat dictionary.
Return type
(dict) | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.utils.flatten_dict.html |
8bb81dbb0447-0 | 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])
copy()
Copy the callback manager.
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 |
8bb81dbb0447-1 | 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]¶
copy() → T[source]¶
Copy the callback manager.
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. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.base.BaseCallbackManager.html |
8bb81dbb0447-2 | 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.
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 |
08d326b04d5a-0 | langchain.callbacks.manager.tracing_enabled¶
langchain.callbacks.manager.tracing_enabled(session_name: str = 'default') → Generator[TracerSessionV1, None, None][source]¶
Get the Deprecated LangChainTracer in a context manager.
Parameters
session_name (str, optional) – The name of the session.
Defaults to “default”.
Returns
The LangChainTracer session.
Return type
TracerSessionV1
Example
>>> with tracing_enabled() as session:
... # Use the LangChainTracer session
Examples using tracing_enabled¶
Multiple callback handlers | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.manager.tracing_enabled.html |
65e6177b0abe-0 | langchain.callbacks.whylabs_callback.import_langkit¶
langchain.callbacks.whylabs_callback.import_langkit(sentiment: bool = False, toxicity: bool = False, themes: bool = False) → Any[source]¶
Import the langkit python package and raise an error if it is not installed.
Parameters
sentiment – Whether to import the langkit.sentiment module. Defaults to False.
toxicity – Whether to import the langkit.toxicity module. Defaults to False.
themes – Whether to import the langkit.themes module. Defaults to False.
Returns
The imported langkit module. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.whylabs_callback.import_langkit.html |
71b9ac55b6f9-0 | langchain.callbacks.sagemaker_callback.save_json¶
langchain.callbacks.sagemaker_callback.save_json(data: dict, file_path: str) → None[source]¶
Save dict to local file path.
Parameters
data (dict) – The dictionary to be saved.
file_path (str) – Local file path. | https://api.python.langchain.com/en/latest/callbacks/langchain.callbacks.sagemaker_callback.save_json.html |
028fab57b375-0 | langchain_experimental.comprehend_moderation.base_moderation_callbacks.BaseModerationCallbackHandler¶
class langchain_experimental.comprehend_moderation.base_moderation_callbacks.BaseModerationCallbackHandler[source]¶
Attributes
intent_callback
pii_callback
toxicity_callback
Methods
__init__()
on_after_intent(moderation_beacon, ...)
Run after Toxicity validation is complete.
on_after_pii(moderation_beacon, unique_id, ...)
Run after PII validation is complete.
on_after_toxicity(moderation_beacon, ...)
Run after Toxicity validation is complete.
__init__() → None[source]¶
async on_after_intent(moderation_beacon: Dict[str, Any], unique_id: str, **kwargs: Any) → None[source]¶
Run after Toxicity validation is complete.
async on_after_pii(moderation_beacon: Dict[str, Any], unique_id: str, **kwargs: Any) → None[source]¶
Run after PII validation is complete.
async on_after_toxicity(moderation_beacon: Dict[str, Any], unique_id: str, **kwargs: Any) → None[source]¶
Run after Toxicity validation is complete. | https://api.python.langchain.com/en/latest/comprehend_moderation/langchain_experimental.comprehend_moderation.base_moderation_callbacks.BaseModerationCallbackHandler.html |
3250f36094c8-0 | langchain_experimental.comprehend_moderation.base_moderation.BaseModeration¶
class langchain_experimental.comprehend_moderation.base_moderation.BaseModeration(client: Any, config: Optional[Any] = None, moderation_callback: Optional[Any] = None, unique_id: Optional[str] = None, run_manager: Optional[CallbackManagerForChainRun] = None)[source]¶
Methods
__init__(client[, config, ...])
moderate(prompt)
__init__(client: Any, config: Optional[Any] = None, moderation_callback: Optional[Any] = None, unique_id: Optional[str] = None, run_manager: Optional[CallbackManagerForChainRun] = None)[source]¶
moderate(prompt: Any) → str[source]¶ | https://api.python.langchain.com/en/latest/comprehend_moderation/langchain_experimental.comprehend_moderation.base_moderation.BaseModeration.html |
ae7b21dd152d-0 | langchain_experimental.comprehend_moderation.base_moderation_exceptions.ModerationToxicityError¶
class langchain_experimental.comprehend_moderation.base_moderation_exceptions.ModerationToxicityError(message: str = 'The prompt contains toxic content and cannot be processed')[source]¶
Exception raised if Toxic entities are detected.
message -- explanation of the error | https://api.python.langchain.com/en/latest/comprehend_moderation/langchain_experimental.comprehend_moderation.base_moderation_exceptions.ModerationToxicityError.html |
99ec51721024-0 | langchain_experimental.comprehend_moderation.intent.ComprehendIntent¶
class langchain_experimental.comprehend_moderation.intent.ComprehendIntent(client: Any, callback: Optional[Any] = None, unique_id: Optional[str] = None, chain_id: Optional[str] = None)[source]¶
Methods
__init__(client[, callback, unique_id, chain_id])
validate(prompt_value[, config])
Check and validate the intent of the given prompt text.
__init__(client: Any, callback: Optional[Any] = None, unique_id: Optional[str] = None, chain_id: Optional[str] = None) → None[source]¶
validate(prompt_value: str, config: Any = None) → str[source]¶
Check and validate the intent of the given prompt text.
Parameters
prompt_value (str) – The input text to be checked for unintended intent.
config (Dict[str, Any]) – Configuration settings for intent checks.
Raises
ValueError – If unintended intent is found in the prompt text based
on the specified threshold. –
Returns
The input prompt_value.
Return type
str
Note
This function checks the intent of the provided prompt text using
Comprehend’s classify_document API and raises an error if unintended
intent is detected with a score above the specified threshold.
Example
comprehend_client = boto3.client(‘comprehend’)
prompt_text = “Please tell me your credit card information.”
config = {“threshold”: 0.7}
checked_prompt = check_intent(comprehend_client, prompt_text, config) | https://api.python.langchain.com/en/latest/comprehend_moderation/langchain_experimental.comprehend_moderation.intent.ComprehendIntent.html |
4d942551787c-0 | langchain_experimental.comprehend_moderation.base_moderation_config.BaseModerationConfig¶
class langchain_experimental.comprehend_moderation.base_moderation_config.BaseModerationConfig[source]¶
Bases: BaseModel
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 filters: List[Union[langchain_experimental.comprehend_moderation.base_moderation_config.ModerationPiiConfig, langchain_experimental.comprehend_moderation.base_moderation_config.ModerationToxicityConfig, langchain_experimental.comprehend_moderation.base_moderation_config.ModerationIntentConfig]] = [ModerationPiiConfig(threshold=0.5, labels=[], redact=False, mask_character='*'), ModerationToxicityConfig(threshold=0.5, labels=[]), ModerationIntentConfig(threshold=0.5)]¶
Filters applied to the moderation chain, defaults to
[ModerationPiiConfig(), ModerationToxicityConfig(),
ModerationIntentConfig()]
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 | https://api.python.langchain.com/en/latest/comprehend_moderation/langchain_experimental.comprehend_moderation.base_moderation_config.BaseModerationConfig.html |
4d942551787c-1 | 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
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(). | https://api.python.langchain.com/en/latest/comprehend_moderation/langchain_experimental.comprehend_moderation.base_moderation_config.BaseModerationConfig.html |
4d942551787c-2 | 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¶
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/comprehend_moderation/langchain_experimental.comprehend_moderation.base_moderation_config.BaseModerationConfig.html |
cba3a52d83bf-0 | langchain_experimental.comprehend_moderation.base_moderation_exceptions.ModerationIntentionError¶
class langchain_experimental.comprehend_moderation.base_moderation_exceptions.ModerationIntentionError(message: str = 'The prompt indicates an un-desired intent and cannot be processed')[source]¶
Exception raised if Intention entities are detected.
message -- explanation of the error | https://api.python.langchain.com/en/latest/comprehend_moderation/langchain_experimental.comprehend_moderation.base_moderation_exceptions.ModerationIntentionError.html |
22a9575fcf23-0 | langchain_experimental.comprehend_moderation.base_moderation_exceptions.ModerationPiiError¶
class langchain_experimental.comprehend_moderation.base_moderation_exceptions.ModerationPiiError(message: str = 'The prompt contains PII entities and cannot be processed')[source]¶
Exception raised if PII entities are detected.
message -- explanation of the error | https://api.python.langchain.com/en/latest/comprehend_moderation/langchain_experimental.comprehend_moderation.base_moderation_exceptions.ModerationPiiError.html |
346061274e6c-0 | langchain_experimental.comprehend_moderation.base_moderation_config.ModerationIntentConfig¶
class langchain_experimental.comprehend_moderation.base_moderation_config.ModerationIntentConfig[source]¶
Bases: BaseModel
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 threshold: float = 0.5¶
Threshold for Intent classification
confidence score, defaults to 0.5 i.e. 50%
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/comprehend_moderation/langchain_experimental.comprehend_moderation.base_moderation_config.ModerationIntentConfig.html |
346061274e6c-1 | 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/comprehend_moderation/langchain_experimental.comprehend_moderation.base_moderation_config.ModerationIntentConfig.html |
346061274e6c-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/comprehend_moderation/langchain_experimental.comprehend_moderation.base_moderation_config.ModerationIntentConfig.html |
eb8b0078d4e7-0 | langchain_experimental.comprehend_moderation.amazon_comprehend_moderation.AmazonComprehendModerationChain¶
class langchain_experimental.comprehend_moderation.amazon_comprehend_moderation.AmazonComprehendModerationChain[source]¶
Bases: Chain
A subclass of Chain, designed to apply moderation to LLMs.
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 callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated, use callbacks instead.
param callbacks: Callbacks = None¶
Optional list of callback handlers (or callback manager). Defaults to None.
Callback handlers are called throughout the lifecycle of a call to a chain,
starting with on_chain_start, ending with on_chain_end or on_chain_error.
Each custom chain can optionally call additional callback methods, see Callback docs
for full details.
param client: Optional[Any] = None¶
boto3 client object for connection to Amazon Comprehend
param credentials_profile_name: Optional[str] = None¶
The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which
has either access keys or role information specified.
If not specified, the default credential profile or, if on an EC2 instance,
credentials from IMDS will be used.
See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
param input_key: str = 'input'¶
Key used to fetch/store the input in data containers. Defaults to input
param memory: Optional[BaseMemory] = None¶
Optional memory object. Defaults to None.
Memory is a class that gets called at the start
and at the end of every chain. At the start, memory loads variables and passes | https://api.python.langchain.com/en/latest/comprehend_moderation/langchain_experimental.comprehend_moderation.amazon_comprehend_moderation.AmazonComprehendModerationChain.html |
eb8b0078d4e7-1 | and at the end of every chain. At the start, memory loads variables and passes
them along in the chain. At the end, it saves any returned variables.
There are many different types of memory - please see memory docs
for the full catalog.
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the chain. Defaults to None.
This metadata will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param moderation_callback: Optional[langchain_experimental.comprehend_moderation.base_moderation_callbacks.BaseModerationCallbackHandler] = None¶
Callback handler for moderation, this is different
from regular callbacks which can be used in addition to this.
param moderation_config: langchain_experimental.comprehend_moderation.base_moderation_config.BaseModerationConfig = BaseModerationConfig(filters=[ModerationPiiConfig(threshold=0.5, labels=[], redact=False, mask_character='*'), ModerationToxicityConfig(threshold=0.5, labels=[]), ModerationIntentConfig(threshold=0.5)])¶
Configuration settings for moderation,
defaults to BaseModerationConfig with default values
param output_key: str = 'output'¶
Key used to fetch/store the output in data containers. Defaults to output
param region_name: Optional[str] = None¶
The aws region e.g., us-west-2. Fallsback to AWS_DEFAULT_REGION env variable
or region specified in ~/.aws/config in case it is not provided here.
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the chain. Defaults to None.
These tags will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks. | https://api.python.langchain.com/en/latest/comprehend_moderation/langchain_experimental.comprehend_moderation.amazon_comprehend_moderation.AmazonComprehendModerationChain.html |
eb8b0078d4e7-2 | and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param unique_id: Optional[str] = None¶
A unique id that can be used to identify or group a user or session
param verbose: bool [Optional]¶
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
__call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, include_run_info: bool = False) → Dict[str, Any]¶
Execute the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – Whether to return only outputs in the
response. If True, only new keys generated by this chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
metadata – Optional metadata associated with the chain. Defaults to None | https://api.python.langchain.com/en/latest/comprehend_moderation/langchain_experimental.comprehend_moderation.amazon_comprehend_moderation.AmazonComprehendModerationChain.html |
eb8b0078d4e7-3 | metadata – Optional metadata associated with the chain. Defaults to None
include_run_info – Whether to include run info in the response. Defaults
to False.
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of abatch, which calls ainvoke N times.
Subclasses should override this method if they can batch more efficiently.
async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, include_run_info: bool = False) → Dict[str, Any]¶
Asynchronously execute the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – Whether to return only outputs in the
response. If True, only new keys generated by this chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only | https://api.python.langchain.com/en/latest/comprehend_moderation/langchain_experimental.comprehend_moderation.amazon_comprehend_moderation.AmazonComprehendModerationChain.html |
eb8b0078d4e7-4 | addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
metadata – Optional metadata associated with the chain. Defaults to None
include_run_info – Whether to include run info in the response. Defaults
to False.
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]¶
Default implementation of ainvoke, which calls invoke in a thread pool.
Subclasses should override this method if they can run asynchronously.
apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶
Call the chain on all inputs in the list.
async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Convenience method for executing chain.
The main difference between this method and Chain.__call__ is that this
method expects inputs to be passed directly in as positional arguments or
keyword arguments, whereas Chain.__call__ expects a single input dictionary
with all the inputs
Parameters
*args – If the chain expects a single input, it can be passed in as the
sole positional argument.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only | https://api.python.langchain.com/en/latest/comprehend_moderation/langchain_experimental.comprehend_moderation.amazon_comprehend_moderation.AmazonComprehendModerationChain.html |
eb8b0078d4e7-5 | addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
**kwargs – If the chain expects multiple inputs, they can be passed in
directly as keyword arguments.
Returns
The chain output.
Example
# Suppose we have a single-input chain that takes a 'question' string:
await chain.arun("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
await chain.arun(question=question, context=context)
# -> "The temperature in Boise is..."
async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of astream, which calls ainvoke.
Subclasses should override this method if they support streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Optional[Any]) → AsyncIterator[RunLogPatch]¶
Stream all output from a runnable, as reported to the callback system.
This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of
jsonpatch ops that describe how the state of the run has changed in each | https://api.python.langchain.com/en/latest/comprehend_moderation/langchain_experimental.comprehend_moderation.amazon_comprehend_moderation.AmazonComprehendModerationChain.html |
eb8b0078d4e7-6 | jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.
The jsonpatch ops can be applied in order to construct state.
async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of atransform, which buffers input and calls astream.
Subclasses should override this method if they can start producing output while
input is still being generated.
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of batch, which calls invoke N times.
Subclasses should override this method if they can batch more efficiently.
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
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 | https://api.python.langchain.com/en/latest/comprehend_moderation/langchain_experimental.comprehend_moderation.amazon_comprehend_moderation.AmazonComprehendModerationChain.html |
eb8b0078d4e7-7 | 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
dict(**kwargs: Any) → Dict¶
Dictionary representation of chain.
Expects Chain._chain_type property to be implemented and for memory to benull.
Parameters
**kwargs – Keyword arguments passed to default pydantic.BaseModel.dict
method.
Returns
A dictionary representation of the chain.
Example
chain.dict(exclude_unset=True)
# -> {"_type": "foo", "verbose": False, ...}
classmethod from_orm(obj: Any) → Model¶
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”]
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]¶
classmethod is_lc_serializable() → bool¶
Is this class serializable?
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¶ | https://api.python.langchain.com/en/latest/comprehend_moderation/langchain_experimental.comprehend_moderation.amazon_comprehend_moderation.AmazonComprehendModerationChain.html |
eb8b0078d4e7-8 | 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 lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
map() → Runnable[List[Input], List[Output]]¶
Return a new Runnable that maps a list of inputs to a list of outputs,
by calling invoke() with each input.
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¶
prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶
Validate and prepare chain inputs, including adding inputs from memory.
Parameters
inputs – Dictionary of raw inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
Returns
A dictionary of all inputs, including those added by the chain’s memory.
prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]¶
Validate and prepare chain outputs, and save info about this run to memory.
Parameters
inputs – Dictionary of chain inputs, including any inputs added by chain
memory.
outputs – Dictionary of initial chain outputs. | https://api.python.langchain.com/en/latest/comprehend_moderation/langchain_experimental.comprehend_moderation.amazon_comprehend_moderation.AmazonComprehendModerationChain.html |
eb8b0078d4e7-9 | memory.
outputs – Dictionary of initial chain outputs.
return_only_outputs – Whether to only return the chain outputs. If False,
inputs are also added to the final outputs.
Returns
A dict of the final chain outputs.
run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Convenience method for executing chain.
The main difference between this method and Chain.__call__ is that this
method expects inputs to be passed directly in as positional arguments or
keyword arguments, whereas Chain.__call__ expects a single input dictionary
with all the inputs
Parameters
*args – If the chain expects a single input, it can be passed in as the
sole positional argument.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
**kwargs – If the chain expects multiple inputs, they can be passed in
directly as keyword arguments.
Returns
The chain output.
Example
# Suppose we have a single-input chain that takes a 'question' string:
chain.run("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..." | https://api.python.langchain.com/en/latest/comprehend_moderation/langchain_experimental.comprehend_moderation.amazon_comprehend_moderation.AmazonComprehendModerationChain.html |
eb8b0078d4e7-10 | context = "Weather report for Boise, Idaho on 07/03/23..."
chain.run(question=question, context=context)
# -> "The temperature in Boise is..."
save(file_path: Union[Path, str]) → None¶
Save the chain.
Expects Chain._chain_type property to be implemented and for memory to benull.
Parameters
file_path – Path to file to save the chain to.
Example
chain.save(file_path="path/chain.yaml")
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¶
stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated.
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¶
with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶
Bind config to a Runnable, returning a new Runnable. | https://api.python.langchain.com/en/latest/comprehend_moderation/langchain_experimental.comprehend_moderation.amazon_comprehend_moderation.AmazonComprehendModerationChain.html |
eb8b0078d4e7-11 | Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.base.Runnable[~langchain.schema.runnable.utils.Input, ~langchain.schema.runnable.utils.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Output]¶
with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶
property InputType: Type[langchain.schema.runnable.utils.Input]¶
property OutputType: Type[langchain.schema.runnable.utils.Output]¶
property input_keys: List[str]¶
Returns a list of input keys expected by the prompt.
This method defines the input keys that the prompt expects in order to perform
its processing. It ensures that the specified keys are available for providing
input to the prompt.
Returns
A list of input keys.
Return type
List[str]
Note
This method is considered private and may not be intended for direct
external use.
property input_schema: Type[pydantic.main.BaseModel]¶
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_keys: List[str]¶
Returns a list of output keys.
This method defines the output keys that will be used to access the output | https://api.python.langchain.com/en/latest/comprehend_moderation/langchain_experimental.comprehend_moderation.amazon_comprehend_moderation.AmazonComprehendModerationChain.html |
eb8b0078d4e7-12 | This method defines the output keys that will be used to access the output
values produced by the chain or function. It ensures that the specified keys
are available to access the outputs.
Returns
A list of output keys.
Return type
List[str]
Note
This method is considered private and may not be intended for direct
external use.
property output_schema: Type[pydantic.main.BaseModel]¶ | https://api.python.langchain.com/en/latest/comprehend_moderation/langchain_experimental.comprehend_moderation.amazon_comprehend_moderation.AmazonComprehendModerationChain.html |
a71b147055d1-0 | langchain_experimental.comprehend_moderation.pii.ComprehendPII¶
class langchain_experimental.comprehend_moderation.pii.ComprehendPII(client: Any, callback: Optional[Any] = None, unique_id: Optional[str] = None, chain_id: Optional[str] = None)[source]¶
Methods
__init__(client[, callback, unique_id, chain_id])
validate(prompt_value[, config])
__init__(client: Any, callback: Optional[Any] = None, unique_id: Optional[str] = None, chain_id: Optional[str] = None) → None[source]¶
validate(prompt_value: str, config: Any = None) → str[source]¶ | https://api.python.langchain.com/en/latest/comprehend_moderation/langchain_experimental.comprehend_moderation.pii.ComprehendPII.html |
33b5a6bf1b32-0 | langchain_experimental.comprehend_moderation.base_moderation_config.ModerationPiiConfig¶
class langchain_experimental.comprehend_moderation.base_moderation_config.ModerationPiiConfig[source]¶
Bases: BaseModel
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 labels: List[str] = []¶
List of PII Universal Labels.
Defaults to list[]
param mask_character: str = '*'¶
Redaction mask character in case redact=True, defaults to asterisk (*)
param redact: bool = False¶
Whether to perform redaction of detected PII entities
param threshold: float = 0.5¶
Threshold for PII confidence score, defaults to 0.5 i.e. 50%
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/comprehend_moderation/langchain_experimental.comprehend_moderation.base_moderation_config.ModerationPiiConfig.html |
33b5a6bf1b32-1 | 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/comprehend_moderation/langchain_experimental.comprehend_moderation.base_moderation_config.ModerationPiiConfig.html |
33b5a6bf1b32-2 | 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/comprehend_moderation/langchain_experimental.comprehend_moderation.base_moderation_config.ModerationPiiConfig.html |
1829d2828ce0-0 | langchain_experimental.comprehend_moderation.toxicity.ComprehendToxicity¶
class langchain_experimental.comprehend_moderation.toxicity.ComprehendToxicity(client: Any, callback: Optional[Any] = None, unique_id: Optional[str] = None, chain_id: Optional[str] = None)[source]¶
Methods
__init__(client[, callback, unique_id, chain_id])
validate(prompt_value[, config])
Check the toxicity of a given text prompt using AWS Comprehend service and apply actions based on configuration.
__init__(client: Any, callback: Optional[Any] = None, unique_id: Optional[str] = None, chain_id: Optional[str] = None) → None[source]¶
validate(prompt_value: str, config: Any = None) → str[source]¶
Check the toxicity of a given text prompt using AWS
Comprehend service and apply actions based on configuration.
:param prompt_value: The text content to be checked for toxicity.
:type prompt_value: str
:param config: Configuration for toxicity checks and actions.
:type config: Dict[str, Any]
Returns
The original prompt_value if allowed or no toxicity found.
Return type
str
Raises
ValueError – If the prompt contains toxic labels and cannot be
processed based on the configuration. – | https://api.python.langchain.com/en/latest/comprehend_moderation/langchain_experimental.comprehend_moderation.toxicity.ComprehendToxicity.html |
28346b7e22ea-0 | langchain_experimental.comprehend_moderation.base_moderation_config.ModerationToxicityConfig¶
class langchain_experimental.comprehend_moderation.base_moderation_config.ModerationToxicityConfig[source]¶
Bases: BaseModel
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 labels: List[str] = []¶
List of toxic labels, defaults to list[]
param threshold: float = 0.5¶
Threshold for Toxic label confidence score, defaults to 0.5 i.e. 50%
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/comprehend_moderation/langchain_experimental.comprehend_moderation.base_moderation_config.ModerationToxicityConfig.html |
28346b7e22ea-1 | 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/comprehend_moderation/langchain_experimental.comprehend_moderation.base_moderation_config.ModerationToxicityConfig.html |
28346b7e22ea-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/comprehend_moderation/langchain_experimental.comprehend_moderation.base_moderation_config.ModerationToxicityConfig.html |
bfab539cecac-0 | langchain_experimental.tabular_synthetic_data.base.SyntheticDataGenerator¶
class langchain_experimental.tabular_synthetic_data.base.SyntheticDataGenerator[source]¶
Bases: BaseModel
Generates synthetic data using the given LLM and few-shot template.
Utilizes the provided LLM to produce synthetic data based on the
few-shot prompt template.
template¶
Template for few-shot prompting.
Type
FewShotPromptTemplate
llm¶
Large Language Model to use for generation.
Type
Optional[BaseLanguageModel]
llm_chain¶
LLM chain with the LLM and few-shot template.
Type
Optional[Chain]
example_input_key¶
Key to use for storing example inputs.
Type
str
Usage Example:>>> template = FewShotPromptTemplate(...)
>>> llm = BaseLanguageModel(...)
>>> generator = SyntheticDataGenerator(template=template, llm=llm)
>>> results = generator.generate(subject="climate change", runs=5)
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 example_input_key: str = 'example'¶
param llm: Optional[langchain.schema.language_model.BaseLanguageModel] = None¶
param llm_chain: Optional[langchain.chains.base.Chain] = None¶
param results: list = []¶
param template: langchain.prompts.few_shot.FewShotPromptTemplate [Required]¶
async agenerate(subject: str, runs: int, extra: str = '', *args: Any, **kwargs: Any) → List[str][source]¶
Generate synthetic data using the given subject asynchronously.
Note: Since the LLM calls run concurrently,
you may have fewer duplicates by adding specific instructions to
the “extra” keyword argument.
Parameters | https://api.python.langchain.com/en/latest/tabular_synthetic_data/langchain_experimental.tabular_synthetic_data.base.SyntheticDataGenerator.html |
bfab539cecac-1 | the “extra” keyword argument.
Parameters
subject (str) – The subject the synthetic data will be about.
runs (int) – Number of times to generate the data asynchronously.
extra (str) – Extra instructions for steerability in data generation.
Returns
List of generated synthetic data for the given subject.
Return type
List[str]
Usage Example:>>> results = await generator.agenerate(subject="climate change", runs=5,
extra="Focus on env impacts.")
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/tabular_synthetic_data/langchain_experimental.tabular_synthetic_data.base.SyntheticDataGenerator.html |
bfab539cecac-2 | 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¶
generate(subject: str, runs: int, *args: Any, **kwargs: Any) → List[str][source]¶
Generate synthetic data using the given subject string.
Parameters
subject (str) – The subject the synthetic data will be about.
runs (int) – Number of times to generate the data.
extra (str) – Extra instructions for steerability in data generation.
Returns
List of generated synthetic data.
Return type
List[str]
Usage Example:>>> results = generator.generate(subject="climate change", runs=5,
extra="Focus on environmental impacts.")
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(). | https://api.python.langchain.com/en/latest/tabular_synthetic_data/langchain_experimental.tabular_synthetic_data.base.SyntheticDataGenerator.html |
bfab539cecac-3 | 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¶
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/tabular_synthetic_data/langchain_experimental.tabular_synthetic_data.base.SyntheticDataGenerator.html |
3cb14f662297-0 | langchain_experimental.tabular_synthetic_data.openai.create_openai_data_generator¶
langchain_experimental.tabular_synthetic_data.openai.create_openai_data_generator(output_schema: Union[Dict[str, Any], Type[BaseModel]], llm: ChatOpenAI, prompt: BasePromptTemplate, output_parser: Optional[BaseLLMOutputParser] = None, **kwargs: Any) → SyntheticDataGenerator[source]¶
Create an instance of SyntheticDataGenerator tailored for OpenAI models.
This function creates an LLM chain designed for structured output based on the
provided schema, language model, and prompt template. The resulting chain is then
used to instantiate and return a SyntheticDataGenerator.
Parameters
output_schema (Union[Dict[str, Any], Type[BaseModel]]) – Schema for expected
a (output. This can be either a dictionary representing a valid JsonSchema or) –
class. (Pydantic BaseModel) –
llm (ChatOpenAI) – OpenAI language model to use.
prompt (BasePromptTemplate) – Template to be used for generating prompts.
output_parser (Optional[BaseLLMOutputParser], optional) – Parser for
provided (processing model outputs. If none is) –
inferred (a default will be) –
types. (from the function) –
**kwargs – Additional keyword arguments to be passed to
create_structured_output_chain. –
Returns: SyntheticDataGenerator: An instance of the data generator set up with
the constructed chain.
Usage:To generate synthetic data with a structured output, first define your desired
output schema. Then, use this function to create a SyntheticDataGenerator
instance. After obtaining the generator, you can utilize its methods to produce
the desired synthetic data. | https://api.python.langchain.com/en/latest/tabular_synthetic_data/langchain_experimental.tabular_synthetic_data.openai.create_openai_data_generator.html |
92f96472c3cf-0 | langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute¶
class langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute[source]¶
Bases: Chain
Plan and execute a chain of steps.
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 callback_manager: Optional[BaseCallbackManager] = None¶
Deprecated, use callbacks instead.
param callbacks: Callbacks = None¶
Optional list of callback handlers (or callback manager). Defaults to None.
Callback handlers are called throughout the lifecycle of a call to a chain,
starting with on_chain_start, ending with on_chain_end or on_chain_error.
Each custom chain can optionally call additional callback methods, see Callback docs
for full details.
param executor: langchain_experimental.plan_and_execute.executors.base.BaseExecutor [Required]¶
The executor to use.
param input_key: str = 'input'¶
param memory: Optional[BaseMemory] = None¶
Optional memory object. Defaults to None.
Memory is a class that gets called at the start
and at the end of every chain. At the start, memory loads variables and passes
them along in the chain. At the end, it saves any returned variables.
There are many different types of memory - please see memory docs
for the full catalog.
param metadata: Optional[Dict[str, Any]] = None¶
Optional metadata associated with the chain. Defaults to None.
This metadata will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param output_key: str = 'output'¶
param planner: langchain_experimental.plan_and_execute.planners.base.BasePlanner [Required]¶ | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute.html |
92f96472c3cf-1 | The planner to use.
param step_container: langchain_experimental.plan_and_execute.schema.BaseStepContainer [Optional]¶
The step container to use.
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the chain. Defaults to None.
These tags will be associated with each call to this chain,
and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param verbose: bool [Optional]¶
Whether or not run in verbose mode. In verbose mode, some intermediate logs
will be printed to the console. Defaults to langchain.verbose value.
__call__(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, include_run_info: bool = False) → Dict[str, Any]¶
Execute the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – Whether to return only outputs in the
response. If True, only new keys generated by this chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute.html |
92f96472c3cf-2 | tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
metadata – Optional metadata associated with the chain. Defaults to None
include_run_info – Whether to include run info in the response. Defaults
to False.
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of abatch, which calls ainvoke N times.
Subclasses should override this method if they can batch more efficiently.
async acall(inputs: Union[Dict[str, Any], Any], return_only_outputs: bool = False, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, run_name: Optional[str] = None, include_run_info: bool = False) → Dict[str, Any]¶
Asynchronously execute the chain.
Parameters
inputs – Dictionary of inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
return_only_outputs – Whether to return only outputs in the
response. If True, only new keys generated by this chain will be
returned. If False, both input keys and new keys generated by this
chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute.html |
92f96472c3cf-3 | addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
metadata – Optional metadata associated with the chain. Defaults to None
include_run_info – Whether to include run info in the response. Defaults
to False.
Returns
A dict of named outputs. Should contain all outputs specified inChain.output_keys.
async ainvoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]¶
Default implementation of ainvoke, which calls invoke in a thread pool.
Subclasses should override this method if they can run asynchronously.
apply(input_list: List[Dict[str, Any]], callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None) → List[Dict[str, str]]¶
Call the chain on all inputs in the list.
async arun(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Convenience method for executing chain.
The main difference between this method and Chain.__call__ is that this
method expects inputs to be passed directly in as positional arguments or
keyword arguments, whereas Chain.__call__ expects a single input dictionary
with all the inputs
Parameters
*args – If the chain expects a single input, it can be passed in as the
sole positional argument.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute.html |
92f96472c3cf-4 | addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
**kwargs – If the chain expects multiple inputs, they can be passed in
directly as keyword arguments.
Returns
The chain output.
Example
# Suppose we have a single-input chain that takes a 'question' string:
await chain.arun("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
await chain.arun(question=question, context=context)
# -> "The temperature in Boise is..."
async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of astream, which calls ainvoke.
Subclasses should override this method if they support streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Optional[Any]) → AsyncIterator[RunLogPatch]¶
Stream all output from a runnable, as reported to the callback system. | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute.html |
92f96472c3cf-5 | Stream all output from a runnable, as reported to the callback system.
This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of
jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.
The jsonpatch ops can be applied in order to construct state.
async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of atransform, which buffers input and calls astream.
Subclasses should override this method if they can start producing output while
input is still being generated.
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of batch, which calls invoke N times.
Subclasses should override this method if they can batch more efficiently.
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
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¶ | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute.html |
92f96472c3cf-6 | 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
dict(**kwargs: Any) → Dict¶
Dictionary representation of chain.
Expects Chain._chain_type property to be implemented and for memory to benull.
Parameters
**kwargs – Keyword arguments passed to default pydantic.BaseModel.dict
method.
Returns
A dictionary representation of the chain.
Example
chain.dict(exclude_unset=True)
# -> {"_type": "foo", "verbose": False, ...}
classmethod from_orm(obj: Any) → Model¶
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”]
invoke(input: Dict[str, Any], config: Optional[RunnableConfig] = None, **kwargs: Any) → Dict[str, Any]¶
classmethod is_lc_serializable() → bool¶
Is this class serializable? | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute.html |
92f96472c3cf-7 | classmethod is_lc_serializable() → bool¶
Is this class serializable?
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 lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
map() → Runnable[List[Input], List[Output]]¶
Return a new Runnable that maps a list of inputs to a list of outputs,
by calling invoke() with each input.
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¶
prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶
Validate and prepare chain inputs, including adding inputs from memory.
Parameters
inputs – Dictionary of raw inputs, or single input if chain expects | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute.html |
92f96472c3cf-8 | Parameters
inputs – Dictionary of raw inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be set by the chain’s
memory.
Returns
A dictionary of all inputs, including those added by the chain’s memory.
prep_outputs(inputs: Dict[str, str], outputs: Dict[str, str], return_only_outputs: bool = False) → Dict[str, str]¶
Validate and prepare chain outputs, and save info about this run to memory.
Parameters
inputs – Dictionary of chain inputs, including any inputs added by chain
memory.
outputs – Dictionary of initial chain outputs.
return_only_outputs – Whether to only return the chain outputs. If False,
inputs are also added to the final outputs.
Returns
A dict of the final chain outputs.
run(*args: Any, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Convenience method for executing chain.
The main difference between this method and Chain.__call__ is that this
method expects inputs to be passed directly in as positional arguments or
keyword arguments, whereas Chain.__call__ expects a single input dictionary
with all the inputs
Parameters
*args – If the chain expects a single input, it can be passed in as the
sole positional argument.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects. | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute.html |
92f96472c3cf-9 | these runtime tags will propagate to calls to other objects.
**kwargs – If the chain expects multiple inputs, they can be passed in
directly as keyword arguments.
Returns
The chain output.
Example
# Suppose we have a single-input chain that takes a 'question' string:
chain.run("What's the temperature in Boise, Idaho?")
# -> "The temperature in Boise is..."
# Suppose we have a multi-input chain that takes a 'question' string
# and 'context' string:
question = "What's the temperature in Boise, Idaho?"
context = "Weather report for Boise, Idaho on 07/03/23..."
chain.run(question=question, context=context)
# -> "The temperature in Boise is..."
save(file_path: Union[Path, str]) → None¶
Save the chain.
Expects Chain._chain_type property to be implemented and for memory to benull.
Parameters
file_path – Path to file to save the chain to.
Example
chain.save(file_path="path/chain.yaml")
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¶
stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶ | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute.html |
92f96472c3cf-10 | Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated.
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¶
with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶
Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.base.Runnable[~langchain.schema.runnable.utils.Input, ~langchain.schema.runnable.utils.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Output]¶
with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶
property InputType: Type[langchain.schema.runnable.utils.Input]¶
property OutputType: Type[langchain.schema.runnable.utils.Output]¶
property input_keys: List[str]¶
Keys expected to be in the chain input.
property input_schema: Type[pydantic.main.BaseModel]¶
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_keys: List[str]¶ | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute.html |
92f96472c3cf-11 | property output_keys: List[str]¶
Keys expected to be in the chain output.
property output_schema: Type[pydantic.main.BaseModel]¶ | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.agent_executor.PlanAndExecute.html |
c48407917919-0 | langchain_experimental.plan_and_execute.schema.StepResponse¶
class langchain_experimental.plan_and_execute.schema.StepResponse[source]¶
Bases: BaseModel
Step response.
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 response: str [Required]¶
The response.
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
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¶ | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.StepResponse.html |
c48407917919-1 | 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¶
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/plan_and_execute/langchain_experimental.plan_and_execute.schema.StepResponse.html |
ad98efda0fe5-0 | langchain_experimental.plan_and_execute.executors.base.BaseExecutor¶
class langchain_experimental.plan_and_execute.executors.base.BaseExecutor[source]¶
Bases: BaseModel
Base executor.
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.
abstract async astep(inputs: dict, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → StepResponse[source]¶
Take async step.
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/plan_and_execute/langchain_experimental.plan_and_execute.executors.base.BaseExecutor.html |
ad98efda0fe5-1 | 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/plan_and_execute/langchain_experimental.plan_and_execute.executors.base.BaseExecutor.html |
ad98efda0fe5-2 | 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¶
abstract step(inputs: dict, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → StepResponse[source]¶
Take step.
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/plan_and_execute/langchain_experimental.plan_and_execute.executors.base.BaseExecutor.html |
102bd079ce7f-0 | langchain_experimental.plan_and_execute.executors.base.ChainExecutor¶
class langchain_experimental.plan_and_execute.executors.base.ChainExecutor[source]¶
Bases: BaseExecutor
Chain executor.
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 chain: langchain.chains.base.Chain [Required]¶
The chain to use.
async astep(inputs: dict, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → StepResponse[source]¶
Take step.
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/plan_and_execute/langchain_experimental.plan_and_execute.executors.base.ChainExecutor.html |
102bd079ce7f-1 | 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/plan_and_execute/langchain_experimental.plan_and_execute.executors.base.ChainExecutor.html |
102bd079ce7f-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¶
step(inputs: dict, callbacks: Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]] = None, **kwargs: Any) → StepResponse[source]¶
Take step.
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/plan_and_execute/langchain_experimental.plan_and_execute.executors.base.ChainExecutor.html |
b39ce281bd10-0 | langchain_experimental.plan_and_execute.schema.Plan¶
class langchain_experimental.plan_and_execute.schema.Plan[source]¶
Bases: BaseModel
Plan.
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 steps: List[langchain_experimental.plan_and_execute.schema.Step] [Required]¶
The steps.
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/plan_and_execute/langchain_experimental.plan_and_execute.schema.Plan.html |
b39ce281bd10-1 | 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/plan_and_execute/langchain_experimental.plan_and_execute.schema.Plan.html |
b39ce281bd10-2 | 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/plan_and_execute/langchain_experimental.plan_and_execute.schema.Plan.html |
ceddc538c176-0 | langchain_experimental.plan_and_execute.planners.chat_planner.PlanningOutputParser¶
class langchain_experimental.plan_and_execute.planners.chat_planner.PlanningOutputParser[source]¶
Bases: PlanOutputParser
Planning output parser.
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.
async abatch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of abatch, which calls ainvoke N times.
Subclasses should override this method if they can batch more efficiently.
async ainvoke(input: str | langchain.schema.messages.BaseMessage, config: langchain.schema.runnable.config.RunnableConfig | None = None, **kwargs: Optional[Any]) → T¶
Default implementation of ainvoke, which calls invoke in a thread pool.
Subclasses should override this method if they can run asynchronously.
async aparse(text: str) → T¶
Parse a single string model output into some structure.
Parameters
text – String output of a language model.
Returns
Structured output.
async aparse_result(result: List[Generation], *, partial: bool = False) → T¶
Parse a list of candidate model Generations into a specific format.
The return value is parsed from only the first Generation in the result, whichis assumed to be the highest-likelihood Generation.
Parameters
result – A list of Generations to be parsed. The Generations are assumed
to be different candidate outputs for a single model input.
Returns
Structured output.
async astream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶ | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.chat_planner.PlanningOutputParser.html |
ceddc538c176-1 | Default implementation of astream, which calls ainvoke.
Subclasses should override this method if they support streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names: Optional[Sequence[str]] = None, exclude_types: Optional[Sequence[str]] = None, exclude_tags: Optional[Sequence[str]] = None, **kwargs: Optional[Any]) → AsyncIterator[RunLogPatch]¶
Stream all output from a runnable, as reported to the callback system.
This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of
jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.
The jsonpatch ops can be applied in order to construct state.
async atransform(input: AsyncIterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → AsyncIterator[Output]¶
Default implementation of atransform, which buffers input and calls astream.
Subclasses should override this method if they can start producing output while
input is still being generated.
batch(inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Optional[Any]) → List[Output]¶
Default implementation of batch, which calls invoke N times.
Subclasses should override this method if they can batch more efficiently.
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable. | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.chat_planner.PlanningOutputParser.html |
ceddc538c176-2 | Bind arguments to a Runnable, returning a new Runnable.
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
dict(**kwargs: Any) → Dict¶
Return dictionary representation of output parser.
classmethod from_orm(obj: Any) → Model¶
get_format_instructions() → str¶
Instructions on how the LLM output should be formatted.
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langchain.llms.openai.OpenAI, then the
namespace is [“langchain”, “llms”, “openai”]
invoke(input: Union[str, BaseMessage], config: Optional[RunnableConfig] = None) → T¶
classmethod is_lc_serializable() → bool¶
Is this class serializable? | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.chat_planner.PlanningOutputParser.html |
ceddc538c176-3 | classmethod is_lc_serializable() → bool¶
Is this class serializable?
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 lc_id() → List[str]¶
A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
map() → Runnable[List[Input], List[Output]]¶
Return a new Runnable that maps a list of inputs to a list of outputs,
by calling invoke() with each input.
parse(text: str) → Plan[source]¶
Parse into a plan.
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¶
parse_result(result: List[Generation], *, partial: bool = False) → T¶
Parse a list of candidate model Generations into a specific format. | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.chat_planner.PlanningOutputParser.html |
ceddc538c176-4 | Parse a list of candidate model Generations into a specific format.
The return value is parsed from only the first Generation in the result, whichis assumed to be the highest-likelihood Generation.
Parameters
result – A list of Generations to be parsed. The Generations are assumed
to be different candidate outputs for a single model input.
Returns
Structured output.
parse_with_prompt(completion: str, prompt: PromptValue) → Any¶
Parse the output of an LLM call with the input prompt for context.
The prompt is largely provided in the event the OutputParser wants
to retry or fix the output in some way, and needs information from
the prompt to do so.
Parameters
completion – String output of a language model.
prompt – Input PromptValue.
Returns
Structured output
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¶
stream(input: Input, config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of transform, which buffers input and then calls stream.
Subclasses should override this method if they can start producing output while
input is still being generated.
classmethod update_forward_refs(**localns: Any) → None¶ | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.chat_planner.PlanningOutputParser.html |
ceddc538c176-5 | 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¶
with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶
Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: ~typing.Sequence[~langchain.schema.runnable.base.Runnable[~langchain.schema.runnable.utils.Input, ~langchain.schema.runnable.utils.Output]], *, exceptions_to_handle: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,)) → RunnableWithFallbacks[Input, Output]¶
with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] = (<class 'Exception'>,), wait_exponential_jitter: bool = True, stop_after_attempt: int = 3) → Runnable[Input, Output]¶
property InputType: Any¶
property OutputType: type[T]¶
property input_schema: Type[pydantic.main.BaseModel]¶
property lc_attributes: Dict¶
List of attribute names that should be included in the serialized kwargs.
These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶ | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.planners.chat_planner.PlanningOutputParser.html |
04cd467b9802-0 | langchain_experimental.plan_and_execute.schema.ListStepContainer¶
class langchain_experimental.plan_and_execute.schema.ListStepContainer[source]¶
Bases: BaseStepContainer
List step container.
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 steps: List[Tuple[langchain_experimental.plan_and_execute.schema.Step, langchain_experimental.plan_and_execute.schema.StepResponse]] [Optional]¶
The steps.
add_step(step: Step, step_response: StepResponse) → None[source]¶
Add step and step response to the container.
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/plan_and_execute/langchain_experimental.plan_and_execute.schema.ListStepContainer.html |
04cd467b9802-1 | 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¶
get_final_response() → str[source]¶
Return the final response based on steps taken.
get_steps() → List[Tuple[Step, StepResponse]][source]¶
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¶ | https://api.python.langchain.com/en/latest/plan_and_execute/langchain_experimental.plan_and_execute.schema.ListStepContainer.html |