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913c896598
Add ruff rules [FBT001](https://docs.astral.sh/ruff/rules/boolean-type-hint-positional-argument/) and [FBT002](https://docs.astral.sh/ruff/rules/boolean-default-value-positional-argument/). Mostly `noqa`s to not introduce breaking changes and possible non-breaking fixes have already been done in a [previous PR](https://github.com/langchain-ai/langchain/pull/29424). These rules will prevent new violations to happen.
86 lines
3.0 KiB
Python
86 lines
3.0 KiB
Python
"""Custom **exceptions** for LangChain."""
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from enum import Enum
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from typing import Any, Optional
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class LangChainException(Exception): # noqa: N818
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"""General LangChain exception."""
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class TracerException(LangChainException):
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"""Base class for exceptions in tracers module."""
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class OutputParserException(ValueError, LangChainException): # noqa: N818
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"""Exception that output parsers should raise to signify a parsing error.
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This exists to differentiate parsing errors from other code or execution errors
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that also may arise inside the output parser. OutputParserExceptions will be
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available to catch and handle in ways to fix the parsing error, while other
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errors will be raised.
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"""
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def __init__(
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self,
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error: Any,
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observation: Optional[str] = None,
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llm_output: Optional[str] = None,
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send_to_llm: bool = False, # noqa: FBT001,FBT002
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):
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"""Create an OutputParserException.
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Args:
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error: The error that's being re-raised or an error message.
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observation: String explanation of error which can be passed to a
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model to try and remediate the issue. Defaults to None.
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llm_output: String model output which is error-ing.
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Defaults to None.
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send_to_llm: Whether to send the observation and llm_output back to an Agent
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after an OutputParserException has been raised.
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This gives the underlying model driving the agent the context that the
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previous output was improperly structured, in the hopes that it will
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update the output to the correct format.
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Defaults to False.
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"""
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if isinstance(error, str):
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error = create_message(
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message=error, error_code=ErrorCode.OUTPUT_PARSING_FAILURE
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)
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super().__init__(error)
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if send_to_llm and (observation is None or llm_output is None):
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msg = (
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"Arguments 'observation' & 'llm_output'"
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" are required if 'send_to_llm' is True"
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)
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raise ValueError(msg)
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self.observation = observation
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self.llm_output = llm_output
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self.send_to_llm = send_to_llm
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class ErrorCode(Enum):
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"""Error codes."""
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INVALID_PROMPT_INPUT = "INVALID_PROMPT_INPUT"
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INVALID_TOOL_RESULTS = "INVALID_TOOL_RESULTS"
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MESSAGE_COERCION_FAILURE = "MESSAGE_COERCION_FAILURE"
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MODEL_AUTHENTICATION = "MODEL_AUTHENTICATION"
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MODEL_NOT_FOUND = "MODEL_NOT_FOUND"
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MODEL_RATE_LIMIT = "MODEL_RATE_LIMIT"
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OUTPUT_PARSING_FAILURE = "OUTPUT_PARSING_FAILURE"
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def create_message(*, message: str, error_code: ErrorCode) -> str:
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"""Create a message with a link to the LangChain troubleshooting guide.
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Args:
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message: The message to display.
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error_code: The error code to display.
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"""
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return (
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f"{message}\n"
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"For troubleshooting, visit: https://python.langchain.com/docs/"
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f"troubleshooting/errors/{error_code.value} "
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)
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