vllm.parser.parser_manager ¶

class ParserManager:
    """
    Provides a unified Parser from the reasoning and tool parser registries.

    Parser engine adapters backed by the same engine are collapsed back into
    that engine. Other parser pairs are composed through ``DelegatingParser``.
    """

    @staticmethod
    def _get_parser_engine_cls(
        parser_cls: type[object] | None,
    ) -> type[ParserEngine] | None:
        if parser_cls is None:
            return None
        parser_engine_cls = getattr(parser_cls, "_parser_engine_cls", None)
        if parser_engine_cls is None:
            return None

        from vllm.parser.engine.parser_engine import ParserEngine

        if not isinstance(parser_engine_cls, type) or not issubclass(
            parser_engine_cls, ParserEngine
        ):
            return None
        return parser_engine_cls

    @classmethod
    def get_tool_parser(
        cls,
        tool_parser_name: str | None = None,
        enable_auto_tools: bool = False,
        model_name: str | None = None,
    ) -> type[ToolParser] | None:
        """Get the tool parser based on the name."""
        from vllm.tool_parsers import ToolParserManager

        parser: type[ToolParser] | None = None
        if not enable_auto_tools or tool_parser_name is None:
            return parser
        logger.info_once('"auto" tool choice has been enabled.')

        try:
            if (
                tool_parser_name == "pythonic"
                and model_name
                and model_name.startswith("meta-llama/Llama-3.2")
            ):
                logger.warning(
                    "Llama3.2 models may struggle to emit valid pythonic tool calls"
                )
            parser = ToolParserManager.get_tool_parser(tool_parser_name)
        except Exception as e:
            raise TypeError(
                "Error: --enable-auto-tool-choice requires "
                f"tool_parser:'{tool_parser_name}' which has not "
                "been registered"
            ) from e
        return parser

    @classmethod
    def get_reasoning_parser(
        cls,
        reasoning_parser_name: str | None,
    ) -> type[ReasoningParser] | None:
        """Get the reasoning parser based on the name."""
        from vllm.reasoning import ReasoningParserManager

        parser: type[ReasoningParser] | None = None
        if not reasoning_parser_name:
            return None
        try:
            parser = ReasoningParserManager.get_reasoning_parser(reasoning_parser_name)
            assert parser is not None
        except Exception as e:
            raise TypeError(f"{reasoning_parser_name=} has not been registered") from e
        return parser

    @classmethod
    def get_parser(
        cls,
        tool_parser_name: str | None = None,
        reasoning_parser_name: str | None = None,
        enable_auto_tools: bool = False,
        model_name: str | None = None,
        is_harmony: bool = False,
    ) -> type[Parser] | None:
        """
        Get a Parser that handles both reasoning and tool parsing.

        Reuses a shared parser engine when possible, otherwise composes the
        individual parsers into a ``DelegatingParser`` subclass.

        Args:
            tool_parser_name: The name of the tool parser.
            reasoning_parser_name: The name of the reasoning parser.
            enable_auto_tools: Whether auto tool choice is enabled.
            model_name: The model name for parser-specific warnings.
            is_harmony: Whether the selected model uses the Harmony format.
                        If True, HarmonyParser is always returned.

        Returns:
            A Parser class, or None if neither parser is specified.
        """
        if not tool_parser_name and not reasoning_parser_name:
            return None

        reasoning_parser_cls = cls.get_reasoning_parser(reasoning_parser_name)
        tool_parser_cls = cls.get_tool_parser(
            tool_parser_name, enable_auto_tools, model_name
        )

        if reasoning_parser_cls is None and tool_parser_cls is None:
            return None

        if is_harmony:
            from vllm.parser.harmony import HarmonyParser

            HarmonyParser.reasoning_parser_cls = reasoning_parser_cls
            HarmonyParser.tool_parser_cls = tool_parser_cls
            return HarmonyParser

        reasoning_engine_cls = cls._get_parser_engine_cls(reasoning_parser_cls)
        tool_engine_cls = cls._get_parser_engine_cls(tool_parser_cls)
        if reasoning_engine_cls is not None and reasoning_engine_cls is tool_engine_cls:
            return reasoning_engine_cls

        if reasoning_parser_name == "kimi_k3" or tool_parser_name == "kimi_k3":
            from vllm.parser.kimi_k3 import KimiK3Parser

            r_cls = reasoning_parser_cls
            t_cls = tool_parser_cls

            class _KimiK3Parser(KimiK3Parser):
                reasoning_parser_cls = r_cls
                tool_parser_cls = t_cls

            return _KimiK3Parser

        from vllm.parser.abstract_parser import DelegatingParser

        r_cls = reasoning_parser_cls
        t_cls = tool_parser_cls

        class _Parser(DelegatingParser):
            reasoning_parser_cls = r_cls
            tool_parser_cls = t_cls

        return _Parser