Source code for langchain_core.exceptions

"""Custom **exceptions** for LangChain."""

from enum import Enum
from typing import Any, Optional




class LangChainException(Exception):  # noqa: N818
    """General LangChain exception."""





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





class OutputParserException(ValueError, LangChainException):  # noqa: N818
    """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.

    Parameters:
        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. Defaults to None.
        llm_output: String model output which is error-ing.
            Defaults to None.
        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. Defaults to False.
    """

    def __init__(
        self,
        error: Any,
        observation: Optional[str] = None,
        llm_output: Optional[str] = None,
        send_to_llm: bool = False,
    ):
        if isinstance(error, str):
            error = create_message(
                message=error, error_code=ErrorCode.OUTPUT_PARSING_FAILURE
            )
        super().__init__(error)
        if send_to_llm and (observation is None or llm_output is None):
            msg = (
                "Arguments 'observation' & 'llm_output'"
                " are required if 'send_to_llm' is True"
            )
            raise ValueError(msg)
        self.observation = observation
        self.llm_output = llm_output
        self.send_to_llm = send_to_llm





class ErrorCode(Enum):
    INVALID_PROMPT_INPUT = "INVALID_PROMPT_INPUT"
    INVALID_TOOL_RESULTS = "INVALID_TOOL_RESULTS"
    MESSAGE_COERCION_FAILURE = "MESSAGE_COERCION_FAILURE"
    MODEL_AUTHENTICATION = "MODEL_AUTHENTICATION"
    MODEL_NOT_FOUND = "MODEL_NOT_FOUND"
    MODEL_RATE_LIMIT = "MODEL_RATE_LIMIT"
    OUTPUT_PARSING_FAILURE = "OUTPUT_PARSING_FAILURE"





def create_message(*, message: str, error_code: ErrorCode) -> str:
    return (
        f"{message}\n"
        "For troubleshooting, visit: https://python.langchain.com/docs/"
        f"troubleshooting/errors/{error_code.value}"
    )