StructuredChatOutputParserWithRetries Class — langchain Architecture

Source Code

libs/langchain/langchain_classic/agents/structured_chat/output_parser.py lines 62–112

class StructuredChatOutputParserWithRetries(AgentOutputParser):
    """Output parser with retries for the structured chat agent."""

    base_parser: AgentOutputParser = Field(default_factory=StructuredChatOutputParser)
    """The base parser to use."""
    output_fixing_parser: OutputFixingParser | None = None
    """The output fixing parser to use."""

    @override
    def get_format_instructions(self) -> str:
        return FORMAT_INSTRUCTIONS

    @override
    def parse(self, text: str) -> AgentAction | AgentFinish:
        try:
            if self.output_fixing_parser is not None:
                return self.output_fixing_parser.parse(text)
            return self.base_parser.parse(text)
        except Exception as e:
            msg = f"Could not parse LLM output: {text}"
            raise OutputParserException(msg) from e

    @classmethod
    def from_llm(
        cls,
        llm: BaseLanguageModel | None = None,
        base_parser: StructuredChatOutputParser | None = None,
    ) -> StructuredChatOutputParserWithRetries:
        """Create a StructuredChatOutputParserWithRetries from a language model.

        Args:
            llm: The language model to use.
            base_parser: An optional StructuredChatOutputParser to use.

        Returns:
            An instance of StructuredChatOutputParserWithRetries.
        """
        if llm is not None:
            base_parser = base_parser or StructuredChatOutputParser()
            output_fixing_parser: OutputFixingParser = OutputFixingParser.from_llm(
                llm=llm,
                parser=base_parser,
            )
            return cls(output_fixing_parser=output_fixing_parser)
        if base_parser is not None:
            return cls(base_parser=base_parser)
        return cls()

    @property
    def _type(self) -> str:
        return "structured_chat_with_retries"