myenv/Lib/site-packages/langchain_core/exceptions.py

"""Custom **exceptions** for LangChain. """
from typing import Any, Optional


class LangChainException(Exception):
    """General LangChain exception."""


class TracerException(LangChainException):
    """Base class for exceptions in tracers module."""


class OutputParserException(ValueError, LangChainException):
    """Exception that output parsers should raise to signify a parsing error.

    This exists to differentiate parsing errors from other code or execution errors
    that also may arise inside the output parser. OutputParserExceptions will be
    available to catch and handle in ways to fix the parsing error, while other
    errors will be raised.

    Args:
        error: The error that's being re-raised or an error message.
        observation: String explanation of error which can be passed to a
            model to try and remediate the issue.
        llm_output: String model output which is error-ing.
        send_to_llm: Whether to send the observation and llm_output back to an Agent
            after an OutputParserException has been raised. This gives the underlying
            model driving the agent the context that the previous output was improperly
            structured, in the hopes that it will update the output to the correct
            format.
    """

    def __init__(
        self,
        error: Any,
        observation: Optional[str] = None,
        llm_output: Optional[str] = None,
        send_to_llm: bool = False,
    ):
        super(OutputParserException, self).__init__(error)
        if send_to_llm:
            if observation is None or llm_output is None:
                raise ValueError(
                    "Arguments 'observation' & 'llm_output'"
                    " are required if 'send_to_llm' is True"
                )
        self.observation = observation
        self.llm_output = llm_output
        self.send_to_llm = send_to_llm