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property ignore_chat_model: bool¶
Whether to ignore chat model callbacks.
property ignore_llm: bool¶
Whether to ignore LLM callbacks.
property ignore_retriever: bool¶
Whether to ignore retriever callbacks.
raise_error: bool = False¶
run_inline: bool = False¶
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.stdout.StdOutCallbackHandler.html
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bd71846cda40-0
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langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler¶
class langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler[source]¶
Bases: AsyncCallbackHandler
Callback handler that returns an async iterator.
Methods
__init__()
aiter()
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.
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_text(text, *, run_id[, parent_run_id, tags])
Run on arbitrary text.
on_tool_end(output, *, run_id[, ...])
Run when tool ends running.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.html
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bd71846cda40-1
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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.
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.
raise_error
run_inline
queue
done
async aiter() → AsyncIterator[str][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: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None¶
Run when chain errors.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.html
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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.
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: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
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: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None¶
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.html
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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.
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: Union[Exception, KeyboardInterrupt], *, 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.
property always_verbose: bool¶
done: asyncio.locks.Event¶
property ignore_agent: bool¶
Whether to ignore agent callbacks.
property ignore_chain: bool¶
Whether to ignore chain callbacks.
property ignore_chat_model: bool¶
Whether to ignore chat model callbacks.
property ignore_llm: bool¶
Whether to ignore LLM callbacks.
property ignore_retriever: bool¶
Whether to ignore retriever callbacks.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.html
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property ignore_retriever: bool¶
Whether to ignore retriever callbacks.
queue: asyncio.queues.Queue[str]¶
raise_error: bool = False¶
run_inline: bool = False¶
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.streaming_aiter.AsyncIteratorCallbackHandler.html
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92b819c57247-0
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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]¶
Bases: AsyncIteratorCallbackHandler
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?
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.
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)
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.streaming_aiter_final_only.AsyncFinalIteratorCallbackHandler.html
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92b819c57247-1
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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_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.
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.
raise_error
run_inline
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.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.streaming_aiter_final_only.AsyncFinalIteratorCallbackHandler.html
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92b819c57247-2
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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: Union[Exception, KeyboardInterrupt], *, 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.
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: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None¶
Run when LLM errors.
async on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.streaming_aiter_final_only.AsyncFinalIteratorCallbackHandler.html
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92b819c57247-3
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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: Union[Exception, KeyboardInterrupt], *, 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.
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: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, **kwargs: Any) → None¶
Run when tool errors.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.streaming_aiter_final_only.AsyncFinalIteratorCallbackHandler.html
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92b819c57247-4
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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.
property always_verbose: bool¶
done: asyncio.Event¶
property ignore_agent: bool¶
Whether to ignore agent callbacks.
property ignore_chain: bool¶
Whether to ignore chain callbacks.
property ignore_chat_model: bool¶
Whether to ignore chat model callbacks.
property ignore_llm: bool¶
Whether to ignore LLM callbacks.
property ignore_retriever: bool¶
Whether to ignore retriever callbacks.
queue: asyncio.Queue[str]¶
raise_error: bool = False¶
run_inline: bool = False¶
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.streaming_aiter_final_only.AsyncFinalIteratorCallbackHandler.html
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964b9f2dcecf-0
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langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler¶
class langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler[source]¶
Bases: BaseCallbackHandler
Callback handler for streaming. Only works with LLMs that support streaming.
Methods
__init__()
on_agent_action(action, **kwargs)
Run on agent action.
on_agent_finish(finish, **kwargs)
Run on agent end.
on_chain_end(outputs, **kwargs)
Run when chain ends running.
on_chain_error(error, **kwargs)
Run when chain errors.
on_chain_start(serialized, inputs, **kwargs)
Run when chain starts running.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, **kwargs)
Run when LLM ends running.
on_llm_error(error, **kwargs)
Run when LLM errors.
on_llm_new_token(token, **kwargs)
Run on new LLM token.
on_llm_start(serialized, prompts, **kwargs)
Run when LLM starts running.
on_retriever_end(documents, *, run_id[, ...])
Run when Retriever ends running.
on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_text(text, **kwargs)
Run on arbitrary text.
on_tool_end(output, **kwargs)
Run when tool ends running.
on_tool_error(error, **kwargs)
Run when tool errors.
on_tool_start(serialized, input_str, **kwargs)
Run when tool starts running.
Attributes
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler.html
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Run when tool starts running.
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.
raise_error
run_inline
on_agent_action(action: AgentAction, **kwargs: Any) → Any[source]¶
Run on agent action.
on_agent_finish(finish: AgentFinish, **kwargs: Any) → None[source]¶
Run on agent end.
on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Run when chain ends running.
on_chain_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when chain errors.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Run when chain starts running.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
Run when LLM ends running.
on_llm_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when LLM errors.
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Run on new LLM token. Only available when streaming is enabled.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler.html
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Run on new LLM token. Only available when streaming is enabled.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
Run when LLM starts running.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever ends running.
on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
on_text(text: str, **kwargs: Any) → None[source]¶
Run on arbitrary text.
on_tool_end(output: str, **kwargs: Any) → None[source]¶
Run when tool ends running.
on_tool_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when tool errors.
on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None[source]¶
Run when tool starts running.
property ignore_agent: bool¶
Whether to ignore agent callbacks.
property ignore_chain: bool¶
Whether to ignore chain callbacks.
property ignore_chat_model: bool¶
Whether to ignore chat model callbacks.
property ignore_llm: bool¶
Whether to ignore LLM callbacks.
property ignore_retriever: bool¶
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler.html
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Whether to ignore LLM callbacks.
property ignore_retriever: bool¶
Whether to ignore retriever callbacks.
raise_error: bool = False¶
run_inline: bool = False¶
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler.html
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6bb7969bc616-0
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langchain.callbacks.streaming_stdout_final_only.FinalStreamingStdOutCallbackHandler¶
class langchain.callbacks.streaming_stdout_final_only.FinalStreamingStdOutCallbackHandler(*, answer_prefix_tokens: Optional[List[str]] = None, strip_tokens: bool = True, stream_prefix: bool = False)[source]¶
Bases: StreamingStdOutCallbackHandler
Callback handler for streaming in agents.
Only works with agents using LLMs that support streaming.
Only the final output of the agent will be streamed.
Instantiate FinalStreamingStdOutCallbackHandler.
Parameters
answer_prefix_tokens – Token sequence that prefixes the answer.
Default is [“Final”, “Answer”, “:”]
strip_tokens – Ignore white spaces and new lines when comparing
answer_prefix_tokens to last tokens? (to determine if answer has been
reached)
stream_prefix – Should answer prefix itself also be streamed?
Methods
__init__(*[, answer_prefix_tokens, ...])
Instantiate FinalStreamingStdOutCallbackHandler.
append_to_last_tokens(token)
check_if_answer_reached()
on_agent_action(action, **kwargs)
Run on agent action.
on_agent_finish(finish, **kwargs)
Run on agent end.
on_chain_end(outputs, **kwargs)
Run when chain ends running.
on_chain_error(error, **kwargs)
Run when chain errors.
on_chain_start(serialized, inputs, **kwargs)
Run when chain starts running.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
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.
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on_llm_new_token(token, **kwargs)
Run on new LLM token.
on_llm_start(serialized, prompts, **kwargs)
Run when LLM starts running.
on_retriever_end(documents, *, run_id[, ...])
Run when Retriever ends running.
on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_text(text, **kwargs)
Run on arbitrary text.
on_tool_end(output, **kwargs)
Run when tool ends running.
on_tool_error(error, **kwargs)
Run when tool errors.
on_tool_start(serialized, input_str, **kwargs)
Run when tool starts running.
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.
raise_error
run_inline
append_to_last_tokens(token: str) → None[source]¶
check_if_answer_reached() → bool[source]¶
on_agent_action(action: AgentAction, **kwargs: Any) → Any¶
Run on agent action.
on_agent_finish(finish: AgentFinish, **kwargs: Any) → None¶
Run on agent end.
on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None¶
Run when chain ends running.
on_chain_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None¶
Run when chain errors.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.streaming_stdout_final_only.FinalStreamingStdOutCallbackHandler.html
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6bb7969bc616-2
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Run when chain errors.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) → None¶
Run when chain starts running.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, **kwargs: Any) → None¶
Run when LLM ends running.
on_llm_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None¶
Run when LLM errors.
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Run on new LLM token. Only available when streaming is enabled.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
Run when LLM starts running.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever ends running.
on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
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Run when Retriever starts running.
on_text(text: str, **kwargs: Any) → None¶
Run on arbitrary text.
on_tool_end(output: str, **kwargs: Any) → None¶
Run when tool ends running.
on_tool_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None¶
Run when tool errors.
on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None¶
Run when tool starts running.
property ignore_agent: bool¶
Whether to ignore agent callbacks.
property ignore_chain: bool¶
Whether to ignore chain callbacks.
property ignore_chat_model: bool¶
Whether to ignore chat model callbacks.
property ignore_llm: bool¶
Whether to ignore LLM callbacks.
property ignore_retriever: bool¶
Whether to ignore retriever callbacks.
raise_error: bool = False¶
run_inline: bool = False¶
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langchain.callbacks.streamlit.mutable_expander.ChildRecord¶
class langchain.callbacks.streamlit.mutable_expander.ChildRecord(type: ChildType, kwargs: Dict[str, Any], dg: DeltaGenerator)[source]¶
Bases: NamedTuple
The child record as a NamedTuple.
Create new instance of ChildRecord(type, kwargs, dg)
Methods
__init__()
count(value, /)
Return number of occurrences of value.
index(value[, start, stop])
Return first index of value.
Attributes
dg
Alias for field number 2
kwargs
Alias for field number 1
type
Alias for field number 0
count(value, /)¶
Return number of occurrences of value.
index(value, start=0, stop=9223372036854775807, /)¶
Return first index of value.
Raises ValueError if the value is not present.
dg: DeltaGenerator¶
Alias for field number 2
kwargs: Dict[str, Any]¶
Alias for field number 1
type: ChildType¶
Alias for field number 0
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langchain.callbacks.streamlit.mutable_expander.ChildType¶
class langchain.callbacks.streamlit.mutable_expander.ChildType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Bases: Enum
The enumerator of the child type.
Attributes
MARKDOWN
EXCEPTION
EXCEPTION = 'EXCEPTION'¶
MARKDOWN = 'MARKDOWN'¶
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langchain.callbacks.streamlit.streamlit_callback_handler.LLMThoughtState¶
class langchain.callbacks.streamlit.streamlit_callback_handler.LLMThoughtState(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]¶
Bases: Enum
Enumerator of the LLMThought state.
Attributes
THINKING
RUNNING_TOOL
COMPLETE
COMPLETE = 'COMPLETE'¶
RUNNING_TOOL = 'RUNNING_TOOL'¶
THINKING = 'THINKING'¶
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langchain.callbacks.streamlit.streamlit_callback_handler.StreamlitCallbackHandler¶
class langchain.callbacks.streamlit.streamlit_callback_handler.StreamlitCallbackHandler(parent_container: DeltaGenerator, *, max_thought_containers: int = 4, expand_new_thoughts: bool = True, collapse_completed_thoughts: bool = True, thought_labeler: Optional[LLMThoughtLabeler] = None)[source]¶
Bases: BaseCallbackHandler
A callback handler that writes to a Streamlit app.
Create a StreamlitCallbackHandler instance.
Parameters
parent_container – The st.container that will contain all the Streamlit elements that the
Handler creates.
max_thought_containers – The max number of completed LLM thought containers to show at once. When
this threshold is reached, a new thought will cause the oldest thoughts to
be collapsed into a “History” expander. Defaults to 4.
expand_new_thoughts – Each LLM “thought” gets its own st.expander. This param controls whether
that expander is expanded by default. Defaults to True.
collapse_completed_thoughts – If True, LLM thought expanders will be collapsed when completed.
Defaults to True.
thought_labeler – An optional custom LLMThoughtLabeler instance. If unspecified, the handler
will use the default thought labeling logic. Defaults to None.
Methods
__init__(parent_container, *[, ...])
Create a StreamlitCallbackHandler instance.
on_agent_action(action[, color])
Run on agent action.
on_agent_finish(finish[, color])
Run on agent end.
on_chain_end(outputs, **kwargs)
Run when chain ends running.
on_chain_error(error, **kwargs)
Run when chain errors.
on_chain_start(serialized, inputs, **kwargs)
Run when chain starts running.
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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 when Retriever ends running.
on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_text(text[, color, end])
Run on arbitrary text.
on_tool_end(output[, color, ...])
Run when tool ends running.
on_tool_error(error, **kwargs)
Run when tool errors.
on_tool_start(serialized, input_str, **kwargs)
Run when tool starts running.
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.
raise_error
run_inline
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.
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Run on agent end.
on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Run when chain ends running.
on_chain_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when chain errors.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Run when chain starts running.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, **kwargs: Any) → None[source]¶
Run when LLM ends running.
on_llm_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when LLM errors.
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Run on new LLM token. Only available when streaming is enabled.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
Run when LLM starts running.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever ends running.
on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
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Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when Retriever starts running.
on_text(text: str, color: Optional[str] = None, end: str = '', **kwargs: Any) → None[source]¶
Run on arbitrary text.
on_tool_end(output: str, color: Optional[str] = None, observation_prefix: Optional[str] = None, llm_prefix: Optional[str] = None, **kwargs: Any) → None[source]¶
Run when tool ends running.
on_tool_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when tool errors.
on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None[source]¶
Run when tool starts running.
property ignore_agent: bool¶
Whether to ignore agent callbacks.
property ignore_chain: bool¶
Whether to ignore chain callbacks.
property ignore_chat_model: bool¶
Whether to ignore chat model callbacks.
property ignore_llm: bool¶
Whether to ignore LLM callbacks.
property ignore_retriever: bool¶
Whether to ignore retriever callbacks.
raise_error: bool = False¶
run_inline: bool = False¶
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langchain.callbacks.streamlit.streamlit_callback_handler.ToolRecord¶
class langchain.callbacks.streamlit.streamlit_callback_handler.ToolRecord(name: str, input_str: str)[source]¶
Bases: NamedTuple
The tool record as a NamedTuple.
Create new instance of ToolRecord(name, input_str)
Methods
__init__()
count(value, /)
Return number of occurrences of value.
index(value[, start, stop])
Return first index of value.
Attributes
input_str
Alias for field number 1
name
Alias for field number 0
count(value, /)¶
Return number of occurrences of value.
index(value, start=0, stop=9223372036854775807, /)¶
Return first index of value.
Raises ValueError if the value is not present.
input_str: str¶
Alias for field number 1
name: str¶
Alias for field number 0
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langchain.callbacks.streamlit.__init__.StreamlitCallbackHandler¶
langchain.callbacks.streamlit.__init__.StreamlitCallbackHandler(parent_container: DeltaGenerator, *, max_thought_containers: int = 4, expand_new_thoughts: bool = True, collapse_completed_thoughts: bool = True, thought_labeler: Optional[LLMThoughtLabeler] = None) → BaseCallbackHandler[source]¶
Construct a new StreamlitCallbackHandler. This CallbackHandler is geared towards
use with a LangChain Agent; it displays the Agent’s LLM and tool-usage “thoughts”
inside a series of Streamlit expanders.
Parameters
parent_container – The st.container that will contain all the Streamlit elements that the
Handler creates.
max_thought_containers – The max number of completed LLM thought containers to show at once. When this
threshold is reached, a new thought will cause the oldest thoughts to be
collapsed into a “History” expander. Defaults to 4.
expand_new_thoughts – Each LLM “thought” gets its own st.expander. This param controls whether that
expander is expanded by default. Defaults to True.
collapse_completed_thoughts – If True, LLM thought expanders will be collapsed when completed.
Defaults to True.
thought_labeler – An optional custom LLMThoughtLabeler instance. If unspecified, the handler
will use the default thought labeling logic. Defaults to None.
Returns
A new StreamlitCallbackHandler instance.
Note that this is an “auto-updating” API (if the installed version of Streamlit)
has a more recent StreamlitCallbackHandler implementation, an instance of that class
will be used.
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langchain.callbacks.tracers.base.BaseTracer¶
class langchain.callbacks.tracers.base.BaseTracer(**kwargs: Any)[source]¶
Bases: BaseCallbackHandler, ABC
Base interface for tracers.
Methods
__init__(**kwargs)
on_agent_action(action, *, run_id[, ...])
Run on agent action.
on_agent_finish(finish, *, run_id[, ...])
Run on agent end.
on_chain_end(outputs, *, run_id, **kwargs)
End a trace for a chain run.
on_chain_error(error, *, run_id, **kwargs)
Handle an error for a chain run.
on_chain_start(serialized, inputs, *, run_id)
Start a trace for a chain run.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, *, run_id, **kwargs)
End a trace for an LLM run.
on_llm_error(error, *, run_id, **kwargs)
Handle an error for an LLM run.
on_llm_new_token(token, *, run_id[, ...])
Run on new LLM token.
on_llm_start(serialized, prompts, *, run_id)
Start a trace for an LLM run.
on_retriever_end(documents, *, run_id, **kwargs)
Run when Retriever ends running.
on_retriever_error(error, *, run_id, **kwargs)
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_text(text, *, run_id[, parent_run_id])
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on_text(text, *, run_id[, parent_run_id])
Run on arbitrary text.
on_tool_end(output, *, run_id, **kwargs)
End a trace for a tool run.
on_tool_error(error, *, run_id, **kwargs)
Handle an error for a tool run.
on_tool_start(serialized, input_str, *, run_id)
Start a trace for a tool run.
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.
raise_error
run_inline
on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent action.
on_agent_finish(finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent end.
on_chain_end(outputs: Dict[str, Any], *, run_id: UUID, **kwargs: Any) → None[source]¶
End a trace for a chain run.
on_chain_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None[source]¶
Handle an error for a chain run.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None[source]¶
Start a trace for a chain run.
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Start a trace for a chain run.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, *, run_id: UUID, **kwargs: Any) → None[source]¶
End a trace for an LLM run.
on_llm_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None[source]¶
Handle an error for an LLM run.
on_llm_new_token(token: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None[source]¶
Run on new LLM token. Only available when streaming is enabled.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None[source]¶
Start a trace for an LLM run.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, **kwargs: Any) → None[source]¶
Run when Retriever ends running.
on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None[source]¶
Run when Retriever errors.
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Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None[source]¶
Run when Retriever starts running.
on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on arbitrary text.
on_tool_end(output: str, *, run_id: UUID, **kwargs: Any) → None[source]¶
End a trace for a tool run.
on_tool_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None[source]¶
Handle an error for a tool run.
on_tool_start(serialized: Dict[str, Any], input_str: str, *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None[source]¶
Start a trace for a tool run.
property ignore_agent: bool¶
Whether to ignore agent callbacks.
property ignore_chain: bool¶
Whether to ignore chain callbacks.
property ignore_chat_model: bool¶
Whether to ignore chat model callbacks.
property ignore_llm: bool¶
Whether to ignore LLM callbacks.
property ignore_retriever: bool¶
Whether to ignore retriever callbacks.
raise_error: bool = False¶
run_inline: bool = False¶
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langchain.callbacks.tracers.base.TracerException¶
class langchain.callbacks.tracers.base.TracerException[source]¶
Bases: Exception
Base class for exceptions in tracers module.
add_note()¶
Exception.add_note(note) –
add a note to the exception
with_traceback()¶
Exception.with_traceback(tb) –
set self.__traceback__ to tb and return self.
args¶
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langchain.callbacks.tracers.evaluation.EvaluatorCallbackHandler¶
class langchain.callbacks.tracers.evaluation.EvaluatorCallbackHandler(evaluators: Sequence[RunEvaluator], max_workers: Optional[int] = None, client: Optional[LangChainPlusClient] = None, example_id: Optional[Union[UUID, str]] = None, skip_unfinished: bool = True, project_name: Optional[str] = None, **kwargs: Any)[source]¶
Bases: BaseTracer
A tracer that runs a run evaluator whenever a run is persisted.
Parameters
evaluators (Sequence[RunEvaluator]) – The run evaluators to apply to all top level runs.
max_workers (int, optional) – The maximum number of worker threads to use for running the evaluators.
If not specified, it will default to the number of evaluators.
client (LangChainPlusClient, optional) – The LangChainPlusClient instance to use for evaluating the runs.
If not specified, a new instance will be created.
example_id (Union[UUID, str], optional) – The example ID to be associated with the runs.
project_name (str, optional) – The LangSmith project name to be organize eval chain runs under.
example_id¶
The example ID associated with the runs.
Type
Union[UUID, None]
client¶
The LangChainPlusClient instance used for evaluating the runs.
Type
LangChainPlusClient
evaluators¶
The sequence of run evaluators to be executed.
Type
Sequence[RunEvaluator]
executor¶
The thread pool executor used for running the evaluators.
Type
ThreadPoolExecutor
futures¶
The set of futures representing the running evaluators.
Type
Set[Future]
skip_unfinished¶
Whether to skip runs that are not finished or raised
an error.
Type
bool
project_name¶
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an error.
Type
bool
project_name¶
The LangSmith project name to be organize eval chain runs under.
Type
Optional[str]
Methods
__init__(evaluators[, max_workers, client, ...])
on_agent_action(action, *, run_id[, ...])
Run on agent action.
on_agent_finish(finish, *, run_id[, ...])
Run on agent end.
on_chain_end(outputs, *, run_id, **kwargs)
End a trace for a chain run.
on_chain_error(error, *, run_id, **kwargs)
Handle an error for a chain run.
on_chain_start(serialized, inputs, *, run_id)
Start a trace for a chain run.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, *, run_id, **kwargs)
End a trace for an LLM run.
on_llm_error(error, *, run_id, **kwargs)
Handle an error for an LLM run.
on_llm_new_token(token, *, run_id[, ...])
Run on new LLM token.
on_llm_start(serialized, prompts, *, run_id)
Start a trace for an LLM run.
on_retriever_end(documents, *, run_id, **kwargs)
Run when Retriever ends running.
on_retriever_error(error, *, run_id, **kwargs)
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_text(text, *, run_id[, parent_run_id])
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on_text(text, *, run_id[, parent_run_id])
Run on arbitrary text.
on_tool_end(output, *, run_id, **kwargs)
End a trace for a tool run.
on_tool_error(error, *, run_id, **kwargs)
Handle an error for a tool run.
on_tool_start(serialized, input_str, *, run_id)
Start a trace for a tool run.
wait_for_futures()
Wait for all futures to complete.
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.
name
raise_error
run_inline
on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent action.
on_agent_finish(finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent end.
on_chain_end(outputs: Dict[str, Any], *, run_id: UUID, **kwargs: Any) → None¶
End a trace for a chain run.
on_chain_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for a chain run.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
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Start a trace for a chain run.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, *, run_id: UUID, **kwargs: Any) → None¶
End a trace for an LLM run.
on_llm_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for an LLM run.
on_llm_new_token(token: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None¶
Run on new LLM token. Only available when streaming is enabled.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Start a trace for an LLM run.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, **kwargs: Any) → None¶
Run when Retriever ends running.
on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Run when Retriever errors.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.tracers.evaluation.EvaluatorCallbackHandler.html
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b299c73be4f5-4
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Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Run when Retriever starts running.
on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on arbitrary text.
on_tool_end(output: str, *, run_id: UUID, **kwargs: Any) → None¶
End a trace for a tool run.
on_tool_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for a tool run.
on_tool_start(serialized: Dict[str, Any], input_str: str, *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Start a trace for a tool run.
wait_for_futures() → None[source]¶
Wait for all futures to complete.
property ignore_agent: bool¶
Whether to ignore agent callbacks.
property ignore_chain: bool¶
Whether to ignore chain callbacks.
property ignore_chat_model: bool¶
Whether to ignore chat model callbacks.
property ignore_llm: bool¶
Whether to ignore LLM callbacks.
property ignore_retriever: bool¶
Whether to ignore retriever callbacks.
name = 'evaluator_callback_handler'¶
raise_error: bool = False¶
run_inline: bool = False¶
run_map: Dict[str, Run]¶
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.tracers.evaluation.EvaluatorCallbackHandler.html
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c757f653e3c8-0
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langchain.callbacks.tracers.langchain.LangChainTracer¶
class langchain.callbacks.tracers.langchain.LangChainTracer(example_id: Optional[Union[UUID, str]] = None, project_name: Optional[str] = None, client: Optional[LangChainPlusClient] = None, tags: Optional[List[str]] = None, **kwargs: Any)[source]¶
Bases: BaseTracer
An implementation of the SharedTracer that POSTS to the langchain endpoint.
Initialize the LangChain tracer.
Methods
__init__([example_id, project_name, client, ...])
Initialize the LangChain tracer.
on_agent_action(action, *, run_id[, ...])
Run on agent action.
on_agent_finish(finish, *, run_id[, ...])
Run on agent end.
on_chain_end(outputs, *, run_id, **kwargs)
End a trace for a chain run.
on_chain_error(error, *, run_id, **kwargs)
Handle an error for a chain run.
on_chain_start(serialized, inputs, *, run_id)
Start a trace for a chain run.
on_chat_model_start(serialized, messages, *, ...)
Start a trace for an LLM run.
on_llm_end(response, *, run_id, **kwargs)
End a trace for an LLM run.
on_llm_error(error, *, run_id, **kwargs)
Handle an error for an LLM run.
on_llm_new_token(token, *, run_id[, ...])
Run on new LLM token.
on_llm_start(serialized, prompts, *, run_id)
Start a trace for an LLM run.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.tracers.langchain.LangChainTracer.html
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c757f653e3c8-1
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Start a trace for an LLM run.
on_retriever_end(documents, *, run_id, **kwargs)
Run when Retriever ends running.
on_retriever_error(error, *, run_id, **kwargs)
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_text(text, *, run_id[, parent_run_id])
Run on arbitrary text.
on_tool_end(output, *, run_id, **kwargs)
End a trace for a tool run.
on_tool_error(error, *, run_id, **kwargs)
Handle an error for a tool run.
on_tool_start(serialized, input_str, *, run_id)
Start a trace for a tool run.
wait_for_futures()
Wait for the given futures to complete.
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.
raise_error
run_inline
on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent action.
on_agent_finish(finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent end.
on_chain_end(outputs: Dict[str, Any], *, run_id: UUID, **kwargs: Any) → None¶
End a trace for a chain run.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.tracers.langchain.LangChainTracer.html
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c757f653e3c8-2
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End a trace for a chain run.
on_chain_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for a chain run.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Start a trace for a chain run.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None[source]¶
Start a trace for an LLM run.
on_llm_end(response: LLMResult, *, run_id: UUID, **kwargs: Any) → None¶
End a trace for an LLM run.
on_llm_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for an LLM run.
on_llm_new_token(token: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None¶
Run on new LLM token. Only available when streaming is enabled.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Start a trace for an LLM run.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.tracers.langchain.LangChainTracer.html
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c757f653e3c8-3
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Start a trace for an LLM run.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, **kwargs: Any) → None¶
Run when Retriever ends running.
on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Run when Retriever starts running.
on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on arbitrary text.
on_tool_end(output: str, *, run_id: UUID, **kwargs: Any) → None¶
End a trace for a tool run.
on_tool_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for a tool run.
on_tool_start(serialized: Dict[str, Any], input_str: str, *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Start a trace for a tool run.
wait_for_futures() → None[source]¶
Wait for the given futures to complete.
property ignore_agent: bool¶
Whether to ignore agent callbacks.
property ignore_chain: bool¶
Whether to ignore chain callbacks.
property ignore_chat_model: bool¶
Whether to ignore chat model callbacks.
property ignore_llm: bool¶
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.tracers.langchain.LangChainTracer.html
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c757f653e3c8-4
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Whether to ignore chat model callbacks.
property ignore_llm: bool¶
Whether to ignore LLM callbacks.
property ignore_retriever: bool¶
Whether to ignore retriever callbacks.
raise_error: bool = False¶
run_inline: bool = False¶
run_map: Dict[str, langchain.callbacks.tracers.schemas.Run]¶
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.tracers.langchain.LangChainTracer.html
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83bb685464d3-0
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langchain.callbacks.tracers.langchain.log_error_once¶
langchain.callbacks.tracers.langchain.log_error_once(method: str, exception: Exception) → None[source]¶
Log an error once.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.tracers.langchain.log_error_once.html
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79e78ece2219-0
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langchain.callbacks.tracers.langchain.wait_for_all_tracers¶
langchain.callbacks.tracers.langchain.wait_for_all_tracers() → None[source]¶
Wait for all tracers to finish.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.tracers.langchain.wait_for_all_tracers.html
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2356ad0105c4-0
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langchain.callbacks.tracers.langchain_v1.get_headers¶
langchain.callbacks.tracers.langchain_v1.get_headers() → Dict[str, Any][source]¶
Get the headers for the LangChain API.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.tracers.langchain_v1.get_headers.html
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5653e3cdfb87-0
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langchain.callbacks.tracers.langchain_v1.LangChainTracerV1¶
class langchain.callbacks.tracers.langchain_v1.LangChainTracerV1(**kwargs: Any)[source]¶
Bases: BaseTracer
An implementation of the SharedTracer that POSTS to the langchain endpoint.
Initialize the LangChain tracer.
Methods
__init__(**kwargs)
Initialize the LangChain tracer.
load_default_session()
Load the default tracing session and set it as the Tracer's session.
load_session(session_name)
Load a session with the given name from the tracer.
on_agent_action(action, *, run_id[, ...])
Run on agent action.
on_agent_finish(finish, *, run_id[, ...])
Run on agent end.
on_chain_end(outputs, *, run_id, **kwargs)
End a trace for a chain run.
on_chain_error(error, *, run_id, **kwargs)
Handle an error for a chain run.
on_chain_start(serialized, inputs, *, run_id)
Start a trace for a chain run.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, *, run_id, **kwargs)
End a trace for an LLM run.
on_llm_error(error, *, run_id, **kwargs)
Handle an error for an LLM run.
on_llm_new_token(token, *, run_id[, ...])
Run on new LLM token.
on_llm_start(serialized, prompts, *, run_id)
Start a trace for an LLM run.
on_retriever_end(documents, *, run_id, **kwargs)
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.tracers.langchain_v1.LangChainTracerV1.html
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5653e3cdfb87-1
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on_retriever_end(documents, *, run_id, **kwargs)
Run when Retriever ends running.
on_retriever_error(error, *, run_id, **kwargs)
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_text(text, *, run_id[, parent_run_id])
Run on arbitrary text.
on_tool_end(output, *, run_id, **kwargs)
End a trace for a tool run.
on_tool_error(error, *, run_id, **kwargs)
Handle an error for a tool run.
on_tool_start(serialized, input_str, *, run_id)
Start a trace for a tool run.
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.
raise_error
run_inline
load_default_session() → Union[TracerSessionV1, TracerSession][source]¶
Load the default tracing session and set it as the Tracer’s session.
load_session(session_name: str) → Union[TracerSessionV1, TracerSession][source]¶
Load a session with the given name from the tracer.
on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent action.
on_agent_finish(finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent end.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.tracers.langchain_v1.LangChainTracerV1.html
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5653e3cdfb87-2
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Run on agent end.
on_chain_end(outputs: Dict[str, Any], *, run_id: UUID, **kwargs: Any) → None¶
End a trace for a chain run.
on_chain_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for a chain run.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Start a trace for a chain run.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, *, run_id: UUID, **kwargs: Any) → None¶
End a trace for an LLM run.
on_llm_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for an LLM run.
on_llm_new_token(token: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None¶
Run on new LLM token. Only available when streaming is enabled.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.tracers.langchain_v1.LangChainTracerV1.html
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5653e3cdfb87-3
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Run on new LLM token. Only available when streaming is enabled.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Start a trace for an LLM run.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, **kwargs: Any) → None¶
Run when Retriever ends running.
on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Run when Retriever starts running.
on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on arbitrary text.
on_tool_end(output: str, *, run_id: UUID, **kwargs: Any) → None¶
End a trace for a tool run.
on_tool_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for a tool run.
on_tool_start(serialized: Dict[str, Any], input_str: str, *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.tracers.langchain_v1.LangChainTracerV1.html
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5653e3cdfb87-4
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Start a trace for a tool run.
property ignore_agent: bool¶
Whether to ignore agent callbacks.
property ignore_chain: bool¶
Whether to ignore chain callbacks.
property ignore_chat_model: bool¶
Whether to ignore chat model callbacks.
property ignore_llm: bool¶
Whether to ignore LLM callbacks.
property ignore_retriever: bool¶
Whether to ignore retriever callbacks.
raise_error: bool = False¶
run_inline: bool = False¶
run_map: Dict[str, langchain.callbacks.tracers.schemas.Run]¶
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.tracers.langchain_v1.LangChainTracerV1.html
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ae3db68e3c42-0
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langchain.callbacks.tracers.run_collector.RunCollectorCallbackHandler¶
class langchain.callbacks.tracers.run_collector.RunCollectorCallbackHandler(example_id: Optional[Union[UUID, str]] = None, **kwargs: Any)[source]¶
Bases: BaseTracer
A tracer that collects all nested runs in a list.
This tracer is useful for inspection and evaluation purposes.
Parameters
example_id (Optional[Union[UUID, str]], default=None) – The ID of the example being traced. It can be either a UUID or a string.
Initialize the RunCollectorCallbackHandler.
Parameters
example_id (Optional[Union[UUID, str]], default=None) – The ID of the example being traced. It can be either a UUID or a string.
Methods
__init__([example_id])
Initialize the RunCollectorCallbackHandler.
on_agent_action(action, *, run_id[, ...])
Run on agent action.
on_agent_finish(finish, *, run_id[, ...])
Run on agent end.
on_chain_end(outputs, *, run_id, **kwargs)
End a trace for a chain run.
on_chain_error(error, *, run_id, **kwargs)
Handle an error for a chain run.
on_chain_start(serialized, inputs, *, run_id)
Start a trace for a chain run.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, *, run_id, **kwargs)
End a trace for an LLM run.
on_llm_error(error, *, run_id, **kwargs)
Handle an error for an LLM run.
on_llm_new_token(token, *, run_id[, ...])
Run on new LLM token.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.tracers.run_collector.RunCollectorCallbackHandler.html
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ae3db68e3c42-1
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Run on new LLM token.
on_llm_start(serialized, prompts, *, run_id)
Start a trace for an LLM run.
on_retriever_end(documents, *, run_id, **kwargs)
Run when Retriever ends running.
on_retriever_error(error, *, run_id, **kwargs)
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_text(text, *, run_id[, parent_run_id])
Run on arbitrary text.
on_tool_end(output, *, run_id, **kwargs)
End a trace for a tool run.
on_tool_error(error, *, run_id, **kwargs)
Handle an error for a tool run.
on_tool_start(serialized, input_str, *, run_id)
Start a trace for a tool run.
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.
name
raise_error
run_inline
on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent action.
on_agent_finish(finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent end.
on_chain_end(outputs: Dict[str, Any], *, run_id: UUID, **kwargs: Any) → None¶
End a trace for a chain run.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.tracers.run_collector.RunCollectorCallbackHandler.html
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ae3db68e3c42-2
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End a trace for a chain run.
on_chain_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for a chain run.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Start a trace for a chain run.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, *, run_id: UUID, **kwargs: Any) → None¶
End a trace for an LLM run.
on_llm_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for an LLM run.
on_llm_new_token(token: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None¶
Run on new LLM token. Only available when streaming is enabled.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Start a trace for an LLM run.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.tracers.run_collector.RunCollectorCallbackHandler.html
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ae3db68e3c42-3
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Start a trace for an LLM run.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, **kwargs: Any) → None¶
Run when Retriever ends running.
on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Run when Retriever starts running.
on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on arbitrary text.
on_tool_end(output: str, *, run_id: UUID, **kwargs: Any) → None¶
End a trace for a tool run.
on_tool_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for a tool run.
on_tool_start(serialized: Dict[str, Any], input_str: str, *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Start a trace for a tool run.
property ignore_agent: bool¶
Whether to ignore agent callbacks.
property ignore_chain: bool¶
Whether to ignore chain callbacks.
property ignore_chat_model: bool¶
Whether to ignore chat model callbacks.
property ignore_llm: bool¶
Whether to ignore LLM callbacks.
property ignore_retriever: bool¶
Whether to ignore retriever callbacks.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.tracers.run_collector.RunCollectorCallbackHandler.html
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ae3db68e3c42-4
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property ignore_retriever: bool¶
Whether to ignore retriever callbacks.
name = 'run-collector_callback_handler'¶
raise_error: bool = False¶
run_inline: bool = False¶
run_map: Dict[str, Run]¶
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.tracers.run_collector.RunCollectorCallbackHandler.html
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langchain.callbacks.tracers.schemas.BaseRun¶
class langchain.callbacks.tracers.schemas.BaseRun(*, uuid: str, parent_uuid: Optional[str] = None, start_time: datetime = None, end_time: datetime = None, extra: Optional[Dict[str, Any]] = None, execution_order: int, child_execution_order: int, serialized: Dict[str, Any], session_id: int, error: Optional[str] = None)[source]¶
Bases: BaseModel
Base class for Run.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param child_execution_order: int [Required]¶
param end_time: datetime.datetime [Optional]¶
param error: Optional[str] = None¶
param execution_order: int [Required]¶
param extra: Optional[Dict[str, Any]] = None¶
param parent_uuid: Optional[str] = None¶
param serialized: Dict[str, Any] [Required]¶
param session_id: int [Required]¶
param start_time: datetime.datetime [Optional]¶
param uuid: str [Required]¶
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langchain.callbacks.tracers.schemas.ChainRun¶
class langchain.callbacks.tracers.schemas.ChainRun(*, uuid: str, parent_uuid: Optional[str] = None, start_time: datetime = None, end_time: datetime = None, extra: Optional[Dict[str, Any]] = None, execution_order: int, child_execution_order: int, serialized: Dict[str, Any], session_id: int, error: Optional[str] = None, inputs: Dict[str, Any], outputs: Optional[Dict[str, Any]] = None, child_llm_runs: List[LLMRun] = None, child_chain_runs: List[ChainRun] = None, child_tool_runs: List[ToolRun] = None)[source]¶
Bases: BaseRun
Class for ChainRun.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param child_chain_runs: List[langchain.callbacks.tracers.schemas.ChainRun] [Optional]¶
param child_execution_order: int [Required]¶
param child_llm_runs: List[langchain.callbacks.tracers.schemas.LLMRun] [Optional]¶
param child_tool_runs: List[langchain.callbacks.tracers.schemas.ToolRun] [Optional]¶
param end_time: datetime.datetime [Optional]¶
param error: Optional[str] = None¶
param execution_order: int [Required]¶
param extra: Optional[Dict[str, Any]] = None¶
param inputs: Dict[str, Any] [Required]¶
param outputs: Optional[Dict[str, Any]] = None¶
param parent_uuid: Optional[str] = None¶
param serialized: Dict[str, Any] [Required]¶
param session_id: int [Required]¶
param start_time: datetime.datetime [Optional]¶
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param start_time: datetime.datetime [Optional]¶
param uuid: str [Required]¶
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langchain.callbacks.tracers.schemas.LLMRun¶
class langchain.callbacks.tracers.schemas.LLMRun(*, uuid: str, parent_uuid: Optional[str] = None, start_time: datetime = None, end_time: datetime = None, extra: Optional[Dict[str, Any]] = None, execution_order: int, child_execution_order: int, serialized: Dict[str, Any], session_id: int, error: Optional[str] = None, prompts: List[str], response: Optional[LLMResult] = None)[source]¶
Bases: BaseRun
Class for LLMRun.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param child_execution_order: int [Required]¶
param end_time: datetime.datetime [Optional]¶
param error: Optional[str] = None¶
param execution_order: int [Required]¶
param extra: Optional[Dict[str, Any]] = None¶
param parent_uuid: Optional[str] = None¶
param prompts: List[str] [Required]¶
param response: Optional[langchain.schema.output.LLMResult] = None¶
param serialized: Dict[str, Any] [Required]¶
param session_id: int [Required]¶
param start_time: datetime.datetime [Optional]¶
param uuid: str [Required]¶
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langchain.callbacks.tracers.schemas.Run¶
class langchain.callbacks.tracers.schemas.Run(*, id: UUID, name: str, start_time: datetime, run_type: str, end_time: Optional[datetime] = None, extra: Optional[dict] = None, error: Optional[str] = None, serialized: Optional[dict] = None, events: Optional[List[Dict]] = None, inputs: dict, outputs: Optional[dict] = None, reference_example_id: Optional[UUID] = None, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, execution_order: int, child_execution_order: int, child_runs: List[Run] = None)[source]¶
Bases: RunBase
Run schema for the V2 API in the Tracer.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param child_execution_order: int [Required]¶
param child_runs: List[langchain.callbacks.tracers.schemas.Run] [Optional]¶
param end_time: Optional[<module 'datetime' from '/home/docs/.asdf/installs/python/3.11.4/lib/python3.11/datetime.py'>] = None¶
param error: Optional[str] = None¶
param events: Optional[List[Dict]] = None¶
param execution_order: int [Required]¶
param extra: Optional[dict] = None¶
param id: uuid.UUID [Required]¶
param inputs: dict [Required]¶
param name: str [Required]¶
param outputs: Optional[dict] = None¶
param parent_run_id: Optional[uuid.UUID] = None¶
param reference_example_id: Optional[uuid.UUID] = None¶
param run_type: str [Required]¶
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param run_type: str [Required]¶
param serialized: Optional[dict] = None¶
param start_time: <module 'datetime' from '/home/docs/.asdf/installs/python/3.11.4/lib/python3.11/datetime.py'> [Required]¶
param tags: Optional[List[str]] [Optional]¶
validator assign_name » all fields[source]¶
Assign name to the run.
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langchain.callbacks.tracers.schemas.ToolRun¶
class langchain.callbacks.tracers.schemas.ToolRun(*, uuid: str, parent_uuid: Optional[str] = None, start_time: datetime = None, end_time: datetime = None, extra: Optional[Dict[str, Any]] = None, execution_order: int, child_execution_order: int, serialized: Dict[str, Any], session_id: int, error: Optional[str] = None, tool_input: str, output: Optional[str] = None, action: str, child_llm_runs: List[LLMRun] = None, child_chain_runs: List[ChainRun] = None, child_tool_runs: List[ToolRun] = None)[source]¶
Bases: BaseRun
Class for ToolRun.
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 action: str [Required]¶
param child_chain_runs: List[langchain.callbacks.tracers.schemas.ChainRun] [Optional]¶
param child_execution_order: int [Required]¶
param child_llm_runs: List[langchain.callbacks.tracers.schemas.LLMRun] [Optional]¶
param child_tool_runs: List[langchain.callbacks.tracers.schemas.ToolRun] [Optional]¶
param end_time: datetime.datetime [Optional]¶
param error: Optional[str] = None¶
param execution_order: int [Required]¶
param extra: Optional[Dict[str, Any]] = None¶
param output: Optional[str] = None¶
param parent_uuid: Optional[str] = None¶
param serialized: Dict[str, Any] [Required]¶
param session_id: int [Required]¶
param start_time: datetime.datetime [Optional]¶
param tool_input: str [Required]¶
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param tool_input: str [Required]¶
param uuid: str [Required]¶
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langchain.callbacks.tracers.schemas.TracerSession¶
class langchain.callbacks.tracers.schemas.TracerSession(*, start_time: datetime = None, name: Optional[str] = None, extra: Optional[Dict[str, Any]] = None, tenant_id: UUID, id: UUID)[source]¶
Bases: TracerSessionBase
TracerSessionV1 schema for the V2 API.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param extra: Optional[Dict[str, Any]] = None¶
param id: uuid.UUID [Required]¶
param name: Optional[str] = None¶
param start_time: datetime.datetime [Optional]¶
param tenant_id: uuid.UUID [Required]¶
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langchain.callbacks.tracers.schemas.TracerSessionBase¶
class langchain.callbacks.tracers.schemas.TracerSessionBase(*, start_time: datetime = None, name: Optional[str] = None, extra: Optional[Dict[str, Any]] = None, tenant_id: UUID)[source]¶
Bases: TracerSessionV1Base
A creation class for TracerSession.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param extra: Optional[Dict[str, Any]] = None¶
param name: Optional[str] = None¶
param start_time: datetime.datetime [Optional]¶
param tenant_id: uuid.UUID [Required]¶
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langchain.callbacks.tracers.schemas.TracerSessionV1¶
class langchain.callbacks.tracers.schemas.TracerSessionV1(*, start_time: datetime = None, name: Optional[str] = None, extra: Optional[Dict[str, Any]] = None, id: int)[source]¶
Bases: TracerSessionV1Base
TracerSessionV1 schema.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param extra: Optional[Dict[str, Any]] = None¶
param id: int [Required]¶
param name: Optional[str] = None¶
param start_time: datetime.datetime [Optional]¶
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.tracers.schemas.TracerSessionV1.html
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langchain.callbacks.tracers.schemas.TracerSessionV1Base¶
class langchain.callbacks.tracers.schemas.TracerSessionV1Base(*, start_time: datetime = None, name: Optional[str] = None, extra: Optional[Dict[str, Any]] = None)[source]¶
Bases: BaseModel
Base class for TracerSessionV1.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param extra: Optional[Dict[str, Any]] = None¶
param name: Optional[str] = None¶
param start_time: datetime.datetime [Optional]¶
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langchain.callbacks.tracers.schemas.TracerSessionV1Create¶
class langchain.callbacks.tracers.schemas.TracerSessionV1Create(*, start_time: datetime = None, name: Optional[str] = None, extra: Optional[Dict[str, Any]] = None)[source]¶
Bases: TracerSessionV1Base
Create class for TracerSessionV1.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parsed to form a valid model.
param extra: Optional[Dict[str, Any]] = None¶
param name: Optional[str] = None¶
param start_time: datetime.datetime [Optional]¶
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langchain.callbacks.tracers.stdout.ConsoleCallbackHandler¶
class langchain.callbacks.tracers.stdout.ConsoleCallbackHandler(**kwargs: Any)[source]¶
Bases: BaseTracer
Tracer that prints to the console.
Methods
__init__(**kwargs)
get_breadcrumbs(run)
get_parents(run)
on_agent_action(action, *, run_id[, ...])
Run on agent action.
on_agent_finish(finish, *, run_id[, ...])
Run on agent end.
on_chain_end(outputs, *, run_id, **kwargs)
End a trace for a chain run.
on_chain_error(error, *, run_id, **kwargs)
Handle an error for a chain run.
on_chain_start(serialized, inputs, *, run_id)
Start a trace for a chain run.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, *, run_id, **kwargs)
End a trace for an LLM run.
on_llm_error(error, *, run_id, **kwargs)
Handle an error for an LLM run.
on_llm_new_token(token, *, run_id[, ...])
Run on new LLM token.
on_llm_start(serialized, prompts, *, run_id)
Start a trace for an LLM run.
on_retriever_end(documents, *, run_id, **kwargs)
Run when Retriever ends running.
on_retriever_error(error, *, run_id, **kwargs)
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
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Run when Retriever starts running.
on_text(text, *, run_id[, parent_run_id])
Run on arbitrary text.
on_tool_end(output, *, run_id, **kwargs)
End a trace for a tool run.
on_tool_error(error, *, run_id, **kwargs)
Handle an error for a tool run.
on_tool_start(serialized, input_str, *, run_id)
Start a trace for a tool run.
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.
name
raise_error
run_inline
get_breadcrumbs(run: Run) → str[source]¶
get_parents(run: Run) → List[Run][source]¶
on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent action.
on_agent_finish(finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent end.
on_chain_end(outputs: Dict[str, Any], *, run_id: UUID, **kwargs: Any) → None¶
End a trace for a chain run.
on_chain_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for a chain run.
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Handle an error for a chain run.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Start a trace for a chain run.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, *, run_id: UUID, **kwargs: Any) → None¶
End a trace for an LLM run.
on_llm_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for an LLM run.
on_llm_new_token(token: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None¶
Run on new LLM token. Only available when streaming is enabled.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Start a trace for an LLM run.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, **kwargs: Any) → None¶
Run when Retriever ends running.
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Run when Retriever ends running.
on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Run when Retriever starts running.
on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on arbitrary text.
on_tool_end(output: str, *, run_id: UUID, **kwargs: Any) → None¶
End a trace for a tool run.
on_tool_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for a tool run.
on_tool_start(serialized: Dict[str, Any], input_str: str, *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Start a trace for a tool run.
property ignore_agent: bool¶
Whether to ignore agent callbacks.
property ignore_chain: bool¶
Whether to ignore chain callbacks.
property ignore_chat_model: bool¶
Whether to ignore chat model callbacks.
property ignore_llm: bool¶
Whether to ignore LLM callbacks.
property ignore_retriever: bool¶
Whether to ignore retriever callbacks.
name = 'console_callback_handler'¶
raise_error: bool = False¶
run_inline: bool = False¶
run_map: Dict[str, Run]¶
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langchain.callbacks.tracers.stdout.elapsed¶
langchain.callbacks.tracers.stdout.elapsed(run: Any) → str[source]¶
Get the elapsed time of a run.
Parameters
run – any object with a start_time and end_time attribute.
Returns
A string with the elapsed time in seconds ormilliseconds if time is less than a second.
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langchain.callbacks.tracers.stdout.try_json_stringify¶
langchain.callbacks.tracers.stdout.try_json_stringify(obj: Any, fallback: str) → str[source]¶
Try to stringify an object to JSON.
:param obj: Object to stringify.
:param fallback: Fallback string to return if the object cannot be stringified.
Returns
A JSON string if the object can be stringified, otherwise the fallback string.
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langchain.callbacks.tracers.wandb.WandbRunArgs¶
class langchain.callbacks.tracers.wandb.WandbRunArgs[source]¶
Bases: TypedDict
Arguments for the WandbTracer.
Methods
__init__(*args, **kwargs)
clear()
copy()
fromkeys([value])
Create a new dictionary with keys from iterable and values set to value.
get(key[, default])
Return the value for key if key is in the dictionary, else default.
items()
keys()
pop(k[,d])
If the key is not found, return the default if given; otherwise, raise a KeyError.
popitem()
Remove and return a (key, value) pair as a 2-tuple.
setdefault(key[, default])
Insert key with a value of default if key is not in the dictionary.
update([E, ]**F)
If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values()
Attributes
job_type
dir
config
project
entity
reinit
tags
group
name
notes
magic
config_exclude_keys
config_include_keys
anonymous
mode
allow_val_change
resume
force
tensorboard
sync_tensorboard
monitor_gym
save_code
id
settings
clear() → None. Remove all items from D.¶
copy() → a shallow copy of D¶
fromkeys(value=None, /)¶
Create a new dictionary with keys from iterable and values set to value.
get(key, default=None, /)¶
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get(key, default=None, /)¶
Return the value for key if key is in the dictionary, else default.
items() → a set-like object providing a view on D's items¶
keys() → a set-like object providing a view on D's keys¶
pop(k[, d]) → v, remove specified key and return the corresponding value.¶
If the key is not found, return the default if given; otherwise,
raise a KeyError.
popitem()¶
Remove and return a (key, value) pair as a 2-tuple.
Pairs are returned in LIFO (last-in, first-out) order.
Raises KeyError if the dict is empty.
setdefault(key, default=None, /)¶
Insert key with a value of default if key is not in the dictionary.
Return the value for key if key is in the dictionary, else default.
update([E, ]**F) → None. Update D from dict/iterable E and F.¶
If E is present and has a .keys() method, then does: for k in E: D[k] = E[k]
If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v
In either case, this is followed by: for k in F: D[k] = F[k]
values() → an object providing a view on D's values¶
allow_val_change: Optional[bool]¶
anonymous: Optional[str]¶
config: Union[Dict, str, None]¶
config_exclude_keys: Optional[List[str]]¶
config_include_keys: Optional[List[str]]¶
dir: Optional[StrPath]¶
entity: Optional[str]¶
force: Optional[bool]¶
group: Optional[str]¶
id: Optional[str]¶
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group: Optional[str]¶
id: Optional[str]¶
job_type: Optional[str]¶
magic: Optional[Union[dict, str, bool]]¶
mode: Optional[str]¶
monitor_gym: Optional[bool]¶
name: Optional[str]¶
notes: Optional[str]¶
project: Optional[str]¶
reinit: Optional[bool]¶
resume: Optional[Union[bool, str]]¶
save_code: Optional[bool]¶
settings: Union[WBSettings, Dict[str, Any], None]¶
sync_tensorboard: Optional[bool]¶
tags: Optional[Sequence]¶
tensorboard: Optional[bool]¶
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langchain.callbacks.tracers.wandb.WandbTracer¶
class langchain.callbacks.tracers.wandb.WandbTracer(run_args: Optional[WandbRunArgs] = None, **kwargs: Any)[source]¶
Bases: BaseTracer
Callback Handler that logs to Weights and Biases.
This handler will log the model architecture and run traces to Weights and Biases.
This will ensure that all LangChain activity is logged to W&B.
Initializes the WandbTracer.
Parameters
run_args – (dict, optional) Arguments to pass to wandb.init(). If not
provided, wandb.init() will be called with no arguments. Please
refer to the wandb.init for more details.
To use W&B to monitor all LangChain activity, add this tracer like any other
LangChain callback:
```
from wandb.integration.langchain import WandbTracer
tracer = WandbTracer()
chain = LLMChain(llm, callbacks=[tracer])
# …end of notebook / script:
tracer.finish()
```
Methods
__init__([run_args])
Initializes the WandbTracer.
finish()
Waits for all asynchronous processes to finish and data to upload.
on_agent_action(action, *, run_id[, ...])
Run on agent action.
on_agent_finish(finish, *, run_id[, ...])
Run on agent end.
on_chain_end(outputs, *, run_id, **kwargs)
End a trace for a chain run.
on_chain_error(error, *, run_id, **kwargs)
Handle an error for a chain run.
on_chain_start(serialized, inputs, *, run_id)
Start a trace for a chain run.
on_chat_model_start(serialized, messages, *, ...)
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on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, *, run_id, **kwargs)
End a trace for an LLM run.
on_llm_error(error, *, run_id, **kwargs)
Handle an error for an LLM run.
on_llm_new_token(token, *, run_id[, ...])
Run on new LLM token.
on_llm_start(serialized, prompts, *, run_id)
Start a trace for an LLM run.
on_retriever_end(documents, *, run_id, **kwargs)
Run when Retriever ends running.
on_retriever_error(error, *, run_id, **kwargs)
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_text(text, *, run_id[, parent_run_id])
Run on arbitrary text.
on_tool_end(output, *, run_id, **kwargs)
End a trace for a tool run.
on_tool_error(error, *, run_id, **kwargs)
Handle an error for a tool run.
on_tool_start(serialized, input_str, *, run_id)
Start a trace for a tool run.
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.
raise_error
run_inline
finish() → None[source]¶
Waits for all asynchronous processes to finish and data to upload.
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Waits for all asynchronous processes to finish and data to upload.
Proxy for wandb.finish().
on_agent_action(action: AgentAction, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent action.
on_agent_finish(finish: AgentFinish, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on agent end.
on_chain_end(outputs: Dict[str, Any], *, run_id: UUID, **kwargs: Any) → None¶
End a trace for a chain run.
on_chain_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for a chain run.
on_chain_start(serialized: Dict[str, Any], inputs: Dict[str, Any], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Start a trace for a chain run.
on_chat_model_start(serialized: Dict[str, Any], messages: List[List[BaseMessage]], *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
Run when a chat model starts running.
on_llm_end(response: LLMResult, *, run_id: UUID, **kwargs: Any) → None¶
End a trace for an LLM run.
on_llm_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for an LLM run.
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Handle an error for an LLM run.
on_llm_new_token(token: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → None¶
Run on new LLM token. Only available when streaming is enabled.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Start a trace for an LLM run.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, **kwargs: Any) → None¶
Run when Retriever ends running.
on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Run when Retriever starts running.
on_text(text: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run on arbitrary text.
on_tool_end(output: str, *, run_id: UUID, **kwargs: Any) → None¶
End a trace for a tool run.
on_tool_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, **kwargs: Any) → None¶
Handle an error for a tool run.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.tracers.wandb.WandbTracer.html
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Handle an error for a tool run.
on_tool_start(serialized: Dict[str, Any], input_str: str, *, run_id: UUID, tags: Optional[List[str]] = None, parent_run_id: Optional[UUID] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → None¶
Start a trace for a tool run.
property ignore_agent: bool¶
Whether to ignore agent callbacks.
property ignore_chain: bool¶
Whether to ignore chain callbacks.
property ignore_chat_model: bool¶
Whether to ignore chat model callbacks.
property ignore_llm: bool¶
Whether to ignore LLM callbacks.
property ignore_retriever: bool¶
Whether to ignore retriever callbacks.
raise_error: bool = False¶
run_inline: bool = False¶
run_map: Dict[str, Run]¶
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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)
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langchain.callbacks.utils.hash_string¶
langchain.callbacks.utils.hash_string(s: str) → str[source]¶
Hash a string using sha1.
Parameters
s (str) – The string to hash.
Returns
The hashed string.
Return type
(str)
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.utils.hash_string.html
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5f949b528792-0
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langchain.callbacks.utils.import_pandas¶
langchain.callbacks.utils.import_pandas() → Any[source]¶
Import the pandas python package and raise an error if it is not installed.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.utils.import_pandas.html
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a426f57ffe0a-0
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langchain.callbacks.utils.import_spacy¶
langchain.callbacks.utils.import_spacy() → Any[source]¶
Import the spacy python package and raise an error if it is not installed.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.utils.import_spacy.html
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langchain.callbacks.utils.import_textstat¶
langchain.callbacks.utils.import_textstat() → Any[source]¶
Import the textstat python package and raise an error if it is not installed.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.utils.import_textstat.html
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56be984ddd6b-0
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langchain.callbacks.utils.load_json¶
langchain.callbacks.utils.load_json(json_path: Union[str, Path]) → str[source]¶
Load json file to a string.
Parameters
json_path (str) – The path to the json file.
Returns
The string representation of the json file.
Return type
(str)
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.utils.load_json.html
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5e4e71660d1e-0
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langchain.callbacks.wandb_callback.analyze_text¶
langchain.callbacks.wandb_callback.analyze_text(text: str, complexity_metrics: bool = True, visualize: bool = True, nlp: Any = None, output_dir: Optional[Union[str, Path]] = None) → dict[source]¶
Analyze text using textstat and spacy.
Parameters
text (str) – The text to analyze.
complexity_metrics (bool) – Whether to compute complexity metrics.
visualize (bool) – Whether to visualize the text.
nlp (spacy.lang) – The spacy language model to use for visualization.
output_dir (str) – The directory to save the visualization files to.
Returns
A dictionary containing the complexity metrics and visualizationfiles serialized in a wandb.Html element.
Return type
(dict)
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.wandb_callback.analyze_text.html
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langchain.callbacks.wandb_callback.construct_html_from_prompt_and_generation¶
langchain.callbacks.wandb_callback.construct_html_from_prompt_and_generation(prompt: str, generation: str) → Any[source]¶
Construct an html element from a prompt and a generation.
Parameters
prompt (str) – The prompt.
generation (str) – The generation.
Returns
The html element.
Return type
(wandb.Html)
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.wandb_callback.construct_html_from_prompt_and_generation.html
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97121e1950f4-0
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langchain.callbacks.wandb_callback.import_wandb¶
langchain.callbacks.wandb_callback.import_wandb() → Any[source]¶
Import the wandb python package and raise an error if it is not installed.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.wandb_callback.import_wandb.html
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cf57c496b516-0
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langchain.callbacks.wandb_callback.load_json_to_dict¶
langchain.callbacks.wandb_callback.load_json_to_dict(json_path: Union[str, Path]) → dict[source]¶
Load json file to a dictionary.
Parameters
json_path (str) – The path to the json file.
Returns
The dictionary representation of the json file.
Return type
(dict)
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.wandb_callback.load_json_to_dict.html
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langchain.callbacks.wandb_callback.WandbCallbackHandler¶
class langchain.callbacks.wandb_callback.WandbCallbackHandler(job_type: Optional[str] = None, project: Optional[str] = 'langchain_callback_demo', entity: Optional[str] = None, tags: Optional[Sequence] = None, group: Optional[str] = None, name: Optional[str] = None, notes: Optional[str] = None, visualize: bool = False, complexity_metrics: bool = False, stream_logs: bool = False)[source]¶
Bases: BaseMetadataCallbackHandler, BaseCallbackHandler
Callback Handler that logs to Weights and Biases.
Parameters
job_type (str) – The type of job.
project (str) – The project to log to.
entity (str) – The entity to log to.
tags (list) – The tags to log.
group (str) – The group to log to.
name (str) – The name of the run.
notes (str) – The notes to log.
visualize (bool) – Whether to visualize the run.
complexity_metrics (bool) – Whether to log complexity metrics.
stream_logs (bool) – Whether to stream callback actions to W&B
This handler will utilize the associated callback method called and formats
the input of each callback function with metadata regarding the state of LLM run,
and adds the response to the list of records for both the {method}_records and
action. It then logs the response using the run.log() method to Weights and Biases.
Initialize callback handler.
Methods
__init__([job_type, project, entity, tags, ...])
Initialize callback handler.
flush_tracker([langchain_asset, reset, ...])
Flush the tracker and reset the session.
get_custom_callback_meta()
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Flush the tracker and reset the session.
get_custom_callback_meta()
on_agent_action(action, **kwargs)
Run on agent action.
on_agent_finish(finish, **kwargs)
Run when agent ends running.
on_chain_end(outputs, **kwargs)
Run when chain ends running.
on_chain_error(error, **kwargs)
Run when chain errors.
on_chain_start(serialized, inputs, **kwargs)
Run when chain starts running.
on_chat_model_start(serialized, messages, *, ...)
Run when a chat model starts running.
on_llm_end(response, **kwargs)
Run when LLM ends running.
on_llm_error(error, **kwargs)
Run when LLM errors.
on_llm_new_token(token, **kwargs)
Run when LLM generates a new token.
on_llm_start(serialized, prompts, **kwargs)
Run when LLM starts.
on_retriever_end(documents, *, run_id[, ...])
Run when Retriever ends running.
on_retriever_error(error, *, run_id[, ...])
Run when Retriever errors.
on_retriever_start(serialized, query, *, run_id)
Run when Retriever starts running.
on_text(text, **kwargs)
Run when agent is ending.
on_tool_end(output, **kwargs)
Run when tool ends running.
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.
Attributes
always_verbose
Whether to call verbose callbacks even if verbose is False.
ignore_agent
Whether to ignore agent callbacks.
ignore_chain
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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.
raise_error
run_inline
flush_tracker(langchain_asset: Any = None, reset: bool = True, finish: bool = False, job_type: Optional[str] = None, project: Optional[str] = None, entity: Optional[str] = None, tags: Optional[Sequence] = None, group: Optional[str] = None, name: Optional[str] = None, notes: Optional[str] = None, visualize: Optional[bool] = None, complexity_metrics: Optional[bool] = None) → None[source]¶
Flush the tracker and reset the session.
Parameters
langchain_asset – The langchain asset to save.
reset – Whether to reset the session.
finish – Whether to finish the run.
job_type – The job type.
project – The project.
entity – The entity.
tags – The tags.
group – The group.
name – The name.
notes – The notes.
visualize – Whether to visualize.
complexity_metrics – Whether to compute complexity metrics.
Returns – None
get_custom_callback_meta() → Dict[str, Any]¶
on_agent_action(action: AgentAction, **kwargs: Any) → Any[source]¶
Run on agent action.
on_agent_finish(finish: AgentFinish, **kwargs: Any) → None[source]¶
Run when agent ends running.
on_chain_end(outputs: Dict[str, Any], **kwargs: Any) → None[source]¶
Run when chain ends running.
on_chain_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when chain errors.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.wandb_callback.WandbCallbackHandler.html
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a9c3a8e68a79-3
|
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: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when LLM errors.
on_llm_new_token(token: str, **kwargs: Any) → None[source]¶
Run when LLM generates a new token.
on_llm_start(serialized: Dict[str, Any], prompts: List[str], **kwargs: Any) → None[source]¶
Run when LLM starts.
on_retriever_end(documents: Sequence[Document], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever ends running.
on_retriever_error(error: Union[Exception, KeyboardInterrupt], *, run_id: UUID, parent_run_id: Optional[UUID] = None, **kwargs: Any) → Any¶
Run when Retriever errors.
on_retriever_start(serialized: Dict[str, Any], query: str, *, run_id: UUID, parent_run_id: Optional[UUID] = None, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, **kwargs: Any) → Any¶
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.wandb_callback.WandbCallbackHandler.html
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a9c3a8e68a79-4
|
Run when Retriever starts running.
on_text(text: str, **kwargs: Any) → None[source]¶
Run when agent is ending.
on_tool_end(output: str, **kwargs: Any) → None[source]¶
Run when tool ends running.
on_tool_error(error: Union[Exception, KeyboardInterrupt], **kwargs: Any) → None[source]¶
Run when tool errors.
on_tool_start(serialized: Dict[str, Any], input_str: str, **kwargs: Any) → None[source]¶
Run when tool starts running.
reset_callback_meta() → None¶
Reset the callback metadata.
property always_verbose: bool¶
Whether to call verbose callbacks even if verbose is False.
property ignore_agent: bool¶
Whether to ignore agent callbacks.
property ignore_chain: bool¶
Whether to ignore chain callbacks.
property ignore_chat_model: bool¶
Whether to ignore chat model callbacks.
property ignore_llm: bool¶
Whether to ignore LLM callbacks.
property ignore_retriever: bool¶
Whether to ignore retriever callbacks.
raise_error: bool = False¶
run_inline: bool = False¶
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.wandb_callback.WandbCallbackHandler.html
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b914ce646dc6-0
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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.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.whylabs_callback.import_langkit.html
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bbb087daf476-0
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langchain.callbacks.whylabs_callback.WhyLabsCallbackHandler¶
class langchain.callbacks.whylabs_callback.WhyLabsCallbackHandler(logger: Logger)[source]¶
Bases: BaseCallbackHandler
Callback Handler for logging to WhyLabs. This callback handler utilizes
langkit to extract features from the prompts & responses when interacting with
an LLM. These features can be used to guardrail, evaluate, and observe interactions
over time to detect issues relating to hallucinations, prompt engineering,
or output validation. LangKit is an LLM monitoring toolkit developed by WhyLabs.
Here are some examples of what can be monitored with LangKit:
* Text Quality
readability score
complexity and grade scores
Text Relevance
- Similarity scores between prompt/responses
- Similarity scores against user-defined themes
- Topic classification
Security and Privacy
- patterns - count of strings matching a user-defined regex pattern group
- jailbreaks - similarity scores with respect to known jailbreak attempts
- prompt injection - similarity scores with respect to known prompt attacks
- refusals - similarity scores with respect to known LLM refusal responses
Sentiment and Toxicity
- sentiment analysis
- toxicity analysis
For more information, see https://docs.whylabs.ai/docs/language-model-monitoring
or check out the LangKit repo here: https://github.com/whylabs/langkit
—
:param api_key: WhyLabs API key. Optional because the preferred
way to specify the API key is with environment variable
WHYLABS_API_KEY.
Parameters
org_id (Optional[str]) – WhyLabs organization id to write profiles to.
Optional because the preferred way to specify the organization id is
with environment variable WHYLABS_DEFAULT_ORG_ID.
dataset_id (Optional[str]) – WhyLabs dataset id to write profiles to.
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rtdocs\api.python.langchain.com\en\latest\callbacks\langchain.callbacks.whylabs_callback.WhyLabsCallbackHandler.html
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